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S1568494613001087 | Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on computer interpretable semantic knowledge. Therefore, social tags are useful for information sharing and organizing, but they lack the computer-interpretability needed to facilitate a personalized social tag recommendation. An interesting issue is how to automatically generate a personalized social tag recommendation list to users when a resource is accessed by users. The novel solution proposed in this study is a hybrid approach based on semantic tag-based resource profile and user preference to provide personalized social tag recommendation. Experiments show that the Precision and Recall of the proposed hybrid approach effectively improves the accuracy of social tag recommendation. | Integrating ontology technology with folksonomies for personalized social tag recommendation |
S1568494613001099 | This paper attempts to solve ore–waste discrimination and block sequencing problems for given capacities through a combination of goal programming (GP) and simulated annealing (SA). The problem is firstly formulated in a goal programming form, which is expressed as the minimization of the violations between the production rates and the installed capacities under access, metal quantity and the required net present value (NPV) constraints. In the model, block sequencing is first solved by CPLEX. The violations from the capacities are allowed under a cost because actual grade and tonnage values of blocks cannot be known. The violations can be accepted to some extent because mines are planned and designed on the basis of simulated/estimated grades. This solution is then submitted to the SA module such that ore–waste discrimination is incorporated into the modeling. A case study was carried out to demonstrate the approach. The findings show that the approach gives rise to the profitability and can be used to generate mining schedules. | Optimizing ore–waste discrimination and block sequencing through simulated annealing |
S1568494613001105 | Support vector regression (SVR) has become very promising and popular in the field of machine learning due to its attractive features and profound empirical performance for small sample, nonlinearity and high dimensional data application. However, most existing support vector regression learning algorithms are limited to the parameters selection and slow learning for large sample. This paper considers an adaptive particle swarm optimization (APSO) algorithm for the parameters selection of support vector regression model. In order to accelerate its training process while keeping high accurate forecasting in each parameters selection step of APSO iteration, an optimal training subset (OTS) method is carried out to choose the representation data points of the full training data set. Furthermore, the optimal parameters setting of SVR and the optimal size of OTS are studied preliminary. Experimental results of an UCI data set and electric load forecasting in New South Wales show that the proposed model is effective and produces better generalization performance. | Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm |
S1568494613001117 | We consider the n-job, k-stage problem in a hybrid flow shop (HFS) with the objective of minimizing the maximum completion time, or makespan, which is an NP-hard problem. An immunoglobulin-based artificial immune system algorithm, called IAIS algorithm, is developed to search for the best sequence. IAIS, which is better fit the natural immune system, improves the existing AIS by the process before/after encounter with antigens. Before encounter with antigens, a new method of somatic recombination is presented; after encounter with antigens, an isotype switching is proposed. The isotype switching is a new approach in artificial immune system, and its purpose is to produce antibodies with the same protection but different function to defense the antigen. To verify IAIS, comparisons with the existing immune-based algorithms and other non-immune-based algorithms are made. Computational results show that IAIS is very competitive for the hybrid flow shop scheduling problem. | An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem |
S1568494613001129 | In this study, a new approach for the formation of type-2 membership functions is introduced. The footprint of uncertainty is formed by using rectangular type-2 fuzzy granules and the resulting membership function is named as granular type-2 membership function. This new approach provides more degrees of freedom and design flexibility in type-2 fuzzy logic systems. Uncertainties on the grades of membership functions can be represented independently for any region in the universe of discourse and free of any functional form. So, the designer could produce nonlinear, discontinuous or hybrid membership functions in granular formation and therefore could model any desired discontinuity and nonlinearity. The effectiveness of the proposed granular type-2 membership functions is firstly demonstrated by simulations done on noise corrupted Mackey–Glass time series prediction. Secondly, flexible design feature of granular type-2 membership functions is illustrated by modeling a nonlinear system having dead zone with uncertain system parameters. The simulation results show that type-2 fuzzy logic systems formed by granular type-2 membership functions have more modeling capabilities than the systems using conventional type-2 membership functions and they are more robust to system parameter changes and noisy inputs. | Granular type-2 membership functions: A new approach to formation of footprint of uncertainty in type-2 fuzzy sets |
S1568494613001130 | Hepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications. | A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease |
S1568494613001154 | This paper proposes a new direction for design optimization of a water distribution network (WDN). The new approach introduces an optimization process to the conceptual design stage of a WDN. The use of multiobjective evolutionary algorithms (MOEAs) for simultaneous topology and sizing design of piping networks is presented. The design problem includes both topological and sizing design variables while the objective functions are network cost and total head loss in pipes. The numerical technique, called a network repairing technique (NRT), is proposed to overcome difficulties in operating MOEAs for network topological design. The problem is then solved by using a number of established and newly developed MOEAs. Also, two new MOEAs namely multiobjective real code population-based incremental learning (RPBIL) and a hybrid algorithm of RPBIL with differential evolution (termed RPBIL–DE) are proposed to tackle the design problems. The optimum results obtained are illustrated and compared. It is shown that the proposed network repairing technique is an efficient and effective tool for topological design of WDNs. Based on the hypervolume indicator, the proposed RPBIL–DE is among the best MOEA performers. | Simultaneous topology and sizing optimization of a water distribution network using a hybrid multiobjective evolutionary algorithm |
S1568494613001166 | This study investigates the feasibility of an ensemble of classifiers in characterizing ultrasonic liver tissue. Texture analysis generally requires feature representation and classification algorithm. From a variety of feature representations and classification algorithms, obtaining optimal ensembles composed of any feature-classifier pairs is difficult. This paper proposes an ensemble creation algorithm that can form an ensemble with high generalization performance. The pattern recognition process comprises four main stages. The first stage utilized multiresolution analysis to extract intrinsic features of ultrasonic liver images. By utilizing spatial-frequency decomposition, a feature vector was obtained by collecting the feature representation for each subimage. In the second stage of the study, various classification algorithms with diverse feature vectors were trained. Based on the trained classifiers, an ensemble was created by using the proposed algorithm in the third stage. The last stage was concerned with the aggregation of individual classifiers. The proposed approach was applied to discriminate ultrasonic liver images from three liver states: normal liver, cirrhosis, and hepatoma. Based on the six well-known fusion schemes, the experimental results showed that the ensemble proposed in this study yields more discrimination. The results indicate that the combining multiple classifiers with different features is an effective approach for characterizing ultrasonic live r tissue. Furthermore, a clinician can use the quantitative index of the classification results when deciding whether to conduct an advanced medical examination, thus improving the quality of medical care. | An ensemble-based data fusion approach for characterizing ultrasonic liver tissue |
S1568494613001178 | We develop a new compatibility for the uncertain additive linguistic preference relations and study its properties which are very suitable to deal with group decision making (GDM) problems involving uncertain additive linguistic preference relations. Based on the linguistic continuous ordered weighted averaging (LCOWA) operator, we present some concepts of the compatibility degree and compatibility index for the two uncertain additive linguistic preference relations. Then, we study some desirable properties including the property that the synthetic uncertain linguistic preference relation is of acceptable compatibility under the condition that uncertain additive linguistic preference relations given by experts are all of acceptable compatibility with the ideal uncertain linguistic preference relation, which provides a theoretic basis for the application of the uncertain additive linguistic preference relations in GDM. In order to determine the weights of experts, we construct an optimal model based on the criterion of minimizing the compatibility index in GDM. Finally, we propose a new approach based on the compatibility index and the expected additive linguistic preference relation to GDM and develop an application of the optimal weights approach compared with the equal weights approach where we analyze a GDM regarding the evaluation of schools in a university. | On compatibility of uncertain additive linguistic preference relations based on the linguistic COWA operator |
S1568494613001191 | Quality control systems focus on maintaining standards in manufactured products. To improve the homogeneity of batches received by final users and to detect manufacturing defaults, a visual control stage is integrated into production line before the packing operation. At this stage, the products are inspected, mostly by human eye, for their obvious external defects; e.g., chips, cracks, scratches, holes and pitting, lumps, spots, notches, and glazing. This task is often referred to as visual inspection which is important to categorize the final products into quality-constant batches. Prototypes are of invaluable help while inspecting the visual attributes of products. Visual quality of products is assessed with respect to these standard units and some quality ratings are made there on the results. However, assessing visual quality is somewhat an ambiguous and troublesome work. Therefore, it will be helpful to utilize a decision making technique, such as fuzzy logic, to facilitate and improve the process. This paper addresses first to the significance of visual inspection and assessment of visual quality of industrial products, and second, gives a unique application of visual quality assessment of vitreous china ceramic sanitary wares by using fuzzy logic method. | Assessing the visual quality of sanitary ware by fuzzy logic |
S1568494613001208 | This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety factors in engineering structures which is described as pessimistic and searching for least favorable responses, in combination with optimization techniques but in contrast to probabilistic approaches. The algorithm is applied and evaluated to be efficient and effective in producing good results via target matching problems: a simulated topology and shape optimization problem where a ‘target’ geometry set is predefined as the Pareto optimal solution and a constrained multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set. | A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems |
S156849461300121X | Merging sustainable development with the business, and taking goals into account from its three dimensions (i.e., economic, environmental and social) which are derived from customer and stakeholder requirements have been a potential source of competitive differentiation for firms. Academic and corporate interest in sustainable supply chain (SSC) management has also risen considerably in recent years. This paper examines the components and elements of SSC management and how they serve as a foundation for an evaluation framework. By using quality function deployment (QFD) as a product/system planning and improvement tool, an effective SSC structure can be obtained. QFD uses a matrix called the “House of Quality” (HoQ), and constructing the HoQ is a critical step in the application of QFD as it translates customer requirements into engineering characteristics. However, participants of HoQ construction sessions tend to provide information about their individual judgments in multiple formats such as numerically or linguistically depending on their different knowledge, experience, culture and circumstance. Furthermore, they can generate incomplete preferences which are challenging to assess in a consistent way. Therefore, the objective of this study is to apply an extended QFD methodology in SSC by introducing a new group decision making (GDM) approach that takes multiple preference formats and incomplete information into account and fusions different formats of expressions into one uniform group decision by means of the fuzzy set theory. To assess the validity of the proposed approach, a case study conducted at HAVI Logistics-Turkey is also presented in the paper. | An integrated QFD framework with multiple formatted and incomplete preferences: A sustainable supply chain application |
S1568494613001221 | Alopex-based evolutionary algorithm (AEA) is one kind of evolutionary algorithm. It possesses the basic characteristics of evolutionary algorithms as well as the advantages of gradient descent methods and simulation anneal algorithm, but it is also easy to trap into a local optimum. For the AEA algorithm, the unreasonably settings of compared population and step length are two typical drawbacks, which lead to the lack of communication between individuals in each generation. In this paper, estimation of distribution algorithm (EDA) is employed to generate the compared population. Then the moving step length in AEA is improved to vary with different phases of the iteration during the actual operation process. And more importantly, harmony search algorithm (HS) is introduced to improve the quality of population of every generation. By compared with original AEA, the performance of the improved algorithm (HSAEA) was tested on 22 unconstrained benchmark functions. The testing results show that HSAEA clearly outperforms original AEA for almost all the benchmark functions. Furthermore, HSAEA is used to estimate reaction kinetic parameters for a heavy oil thermal cracking three lumps model and Homogeneous mercury (Hg) oxidation, and satisfactory results are obtained. | An improved AEA algorithm with Harmony Search(HSAEA) and its application in reaction kinetic parameter estimation |
S1568494613001233 | The concept of intuitionistic fuzzy soft set (IFSS) arising from intuitionistic fuzzy set (IFS) is generalized by including a parameter reflecting a moderator's opinion about the validity of the information provided. The resulting generalized intuitionistic fuzzy soft set (GIFSS) finds a special role in the decision making applications. It can evaluate the given criteria along with the moderator's assessment of the furnished data. The properties of GIFSS are investigated and the associated relations called generalized intuitionistic fuzzy soft relations (GIFSR) are given. A similarity measure is given to compare two GIFSSs. As this is not applicable to fuzzy numbers, a new score function is devised to compare two intuitionistic fuzzy numbers (IFNs), the components of IFS. The effectiveness of the proposed GIFSS in decision making is demonstrated on four case studies. | Generalized intuitionistic fuzzy soft sets with applications in decision-making |
S1568494613001245 | Short-term load forecasting (STLF) is one of the planning strategies adopted in the daily power system operation and control. All though many forecasting models have been developed through the years, the uncertainties present in the load profile significantly degrade the performance of these models. The uncertainties are mainly due to the sensitivity of the load demand with varying weather conditions, consumption pattern during month and day of the year. Therefore, the effect of these weather variables on the load consumption pattern is discussed. Based on the literature survey, artificial neural networks (ANN) models are found to be an alternative to classical statistical methods in terms of accuracy of the forecasted results. However, handling of bulk volumes of historical data and forecasting accuracy is still a major challenge. The development of third generation neural networks such as spike train models which are closer to their biological counterparts is recently emerging as a robust model. So, this paper presents a load forecasting system known as the SNNSTLF (spiking neural network short-term load forecaster). The proposed model has been tested on the database obtained from the Australian Energy Market Operator (AEMO) website for Victoria State. | A spiking neural network (SNN) forecast engine for short-term electrical load forecasting |
S1568494613001257 | Although numerous research studies in recent years have been proposed for comparing and ranking fuzzy numbers, most of the existing approaches suffer from plenty of shortcomings. In particular, they have produced counter-intuitive ranking orders under certain cases, inconsistent ranking orders of the fuzzy numbers’ images, and lack of discrimination power to rank similar and symmetric fuzzy numbers. This study's goal is to propose a new epsilon-deviation degree approach based on the left and right areas of a fuzzy number and the concept of a centroid point to overcome previous drawbacks. The proposed approach defines an epsilon-transfer coefficient to avoid illogicality when ranking fuzzy numbers with identical centroid points and develops two innovative ranking indices to consistently distinguish similar or symmetric fuzzy numbers by considering the decision maker's attitude. The advantages of the proposed method are illustrated through several numerical examples and comparisons with the existing approaches. The results demonstrate that this approach is effective for ranking generalized fuzzy numbers and overcomes the shortcomings in recent studies. | Ranking fuzzy numbers based on epsilon-deviation degree |
S1568494613001269 | Understanding the weaknesses and the limitations of existing digital fingerprinting schemes and designing effective collusion attacks play an important role in the development of digital fingerprinting. In this paper, we propose a collusion attack optimization framework for spread-spectrum (SS) fingerprinting. Our framework is based upon the closed-loop feedback control theory. In the framework, we at first define a measure function to test whether the fingerprint presents in the attacked signal after collusion. Then, an optimization mechanism is introduced to attenuate the fingerprints from the forgery. We evaluate the performance of the proposed framework for three different SS-based embedding methods. The experimental results show that the proposed framework is more effective than the other examined collusion attacks. About three pieces of fingerprinted content are able to interrupt the fingerprinting system which accommodates about 1000 users, if we require the detection probability to be less than 0.9. Meanwhile, a high fidelity of the attacked content is retained. | A collusion attack optimization framework toward spread-spectrum fingerprinting |
S1568494613001270 | Despite of complex and nonlinear relationships imparting soil–wheel interactions, however, logical, non-randomized, and manifold relations tackle to express and model the interactions which are valid for variety of conditions and are likely to be established whereas mathematical equations are restricted to present. A 3-10-1 feed-forward Artificial Neural Network (ANN) with back propagation (BP) learning algorithm was utilized to estimate the rolling resistance of wheel as affected by velocity, tire inflation pressure, and normal load acting on wheel inside the soil bin facility creating controlled condition for test run. The model represented mean squared error MSE of 0.0257 and predicted relative error values with less than 10% and high coefficient of determination (R 2) equal to 0.9322 utilizing experimental output data obtained from single-wheel tester of soil bin facility. These rewarding outcomes signify the fitting exploit of ANN for prediction of rolling resistance as a practical model with high accuracy in clay loam soil. Derived data revealed rolling resistance is less affected by applicable velocities of tractors in farmlands nevertheless is much influenced by inflation pressure and vertical load. An approximate constant relationship existed between velocity and rolling resistance implying that rolling resistance is not function of velocity chiefly in lower ones. Increase of inflation pressure results in decrease of rolling resistance while increase of vertical load brings about increase of rolling resistance which was measured to be function of vertical load by polynomial with order of two model validated by conventional models such as Wismer and Luth model. | Artificial Neural Network estimation of wheel rolling resistance in clay loam soil |
S1568494613001282 | The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature. | A hybrid metaheuristic approach for the capacitated p-median problem |
S1568494613001294 | A hybrid self-adaptive bees algorithm is proposed for the examination timetabling problems. The bees algorithm (BA) is a population-based algorithm inspired by the way that honey bees forage for food. The algorithm presents a type of neighbourhood search that includes a random search that can be used for optimisation problems. In the BA, the bees search randomly for food sites and return back to the hive carrying the information about the food sites (fitness values); then, other bees will select the sites based on their information (more bees are recruited to the best sites) and will start a random search. We propose three techniques (i.e. disruptive, tournament and rank selection strategies) for selecting the sites, rather than using the fitness value, to improve the diversity of the population. Additionally, a self-adaptive strategy for directing the neighbourhood search is added to further enhance the local intensification capability. Finally, a modified bees algorithm is combined with a local search (i.e. simulated annealing, late acceptance hill climbing) to quickly descend to the optimum solution. Experimental results comparing our proposed modifications with each other and with the basic BA show that all of the modifications outperform the basic BA; an overall comparison has been made with the best known results on two examination timetabling benchmark datasets, which shows that our approach is competitive and works well across all of the problem instances. | A hybrid self-adaptive bees algorithm for examination timetabling problems |
S1568494613001312 | This paper proposes a face recognition system to overcome the problem due to illumination variation. The propose system first classifies the image's illumination into dark, normal or shadow and then based on the illumination type; an appropriate technique is applied for illumination normalization. Propose system ensures that there is no loss of features from the image due to a proper selection of illumination normalization technique for illumination compensation. Moreover, it also saves the processing time for illumination normalization process when an image is classified as normal. This makes the approach computationally efficient. Rough Set Theory is used to build rmf illumination classifier for illumination classification. The results obtained as high as 96% in terms of accuracy of correct classification of images as dark, normal or shadow. | Rough membership function based illumination classifier for illumination invariant face recognition |
S1568494613001324 | In this paper, the modelling problem of nonlinear systems with dead-zone input is considered. To solve this problem, an evolving intelligent system is proposed. The uniform stability of the modelling error for the aforementioned system is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed technique is verified by simulations. | Evolving intelligent system for the modelling of nonlinear systems with dead-zone input |
S1568494613001336 | The purpose of current investigation is to engage two efficient evolvable neuro-evolutionary machines to identify a nonlinear dynamic model for a shape memory alloy (SMA) actuator. SMA materials are kind of smart materials capable of compensating any undergo plastic deformations and return to their memorized shape. This fascinating trait gives them versatility to be applied on different engineering applications such as smart actuators and sensors. As a result, modeling and analyzing of their response is an essential task to researchers. Nevertheless, these materials have intricate behaviors that incorporate the modeling with major dilemma and obstacles. In this research, two novel evolvable machines comprised recurrent neural network (RNN) and two novel hybrid heuristic methods nominally cellular automate and Kohonen map assisted versions of The Great Salmon Run (CTGSR and KTGSR respectively) optimization algorithm are developed to find a robust, representative and reliable recursive identification framework capable of modeling the proposed SMA actuator. To elaborate on the acceptable performance of proposed systems, several experimental tests are carried out. Obtained results reveal the promising potential of the evolvable frameworks for modeling the behavior of SMA as a complex real world engineering system. Furthermore, by executing some comparative tests, the authors indicate that both of their proposed hybrid heuristic algorithms outperform the sole version of TGSR as well as some other well-known evolutionary algorithms. | Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature |
S1568494613001348 | Fuzzy neural network (FNN) can be trained with crisp and fuzzy data. This paper presents a novel approach to solve system of fuzzy differential equations (SFDEs) with fuzzy initial values by applying the universal approximation method (UAM) through an artificial intelligence utility in a simple way. The model finds the approximated solution of SFDEs inside of its domain for the close enough neighborhood of the fuzzy initial points. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. At the same time, some examples in engineering and economics are designed. | Fuzzy neural network for solving a system of fuzzy differential equations |
S156849461300135X | A methodology for designing semi-physical fuzzy models is proposed. Prior physical knowledge about the dynamics of the system is modeled with continuous time differential equations. Fuzzy knowledge bases are embedded in these equations as nonlinear constructive blocks. Rules comprising the knowledge bases are fitted to interval-valued data with metaheuristics. A possibilistic filter is proposed that is able to gradually evolve an initial estimation of the latent variables of the model on the basis of successive prediction errors. This methodology has been applied to the prediction of voltage and state of charge of LiFePO4 batteries. An empirical study has been carried over data gathered in experiments at the Battery Laboratory at Oviedo University. Fitting between the proposed model and actual measurements is studied for four different manufacturers and different charge–discharge patterns. Predictions of the evolution of the voltage during charge, discharge and inactivity compare favorably to different models in the literature. The possibilistic filter allows to estimate the state of charge of batteries after an arbitrary path that may include partial charges and discharges. It is shown that the accuracy of the open loop model improves that of other approaches in the literature, and at the same time the observer-based online model is able to approximate the effective remnant charge of the battery after a reasonably short time. | A design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries |
S1568494613001361 | This paper proposes a modified binary particle swarm optimization (MBPSO) method for feature selection with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features. | Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients |
S1568494613001373 | This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulate the input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments. | A fast learning algorithm for evolving neo-fuzzy neuron |
S1568494613001385 | A hybrid hydrologic estimation model is presented with the aim of performing accurate river flow forecasts without the need of using prior knowledge from the experts in the field. The problem of predicting stream flows is a non-trivial task because the various physical mechanisms governing the river flow dynamics act on a wide range of temporal and spatial scales and almost all the mechanisms involved in the river flow process present some degree of nonlinearity. The proposed system incorporates both statistical and artificial intelligence techniques used at different stages of the reasoning cycle in order to calculate the mean daily water volume forecast of the Salvajina reservoir inflow located at the Department of Cauca, Colombia. The accuracy of the proposed model is compared against other well-known artificial intelligence techniques and several statistical tools previously applied in time series forecasting. The results obtained from the experiments carried out using real data from years 1950 to 2006 demonstrate the superiority of the hybrid system. | A hybrid artificial intelligence model for river flow forecasting |
S1568494613001403 | The technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi-attribute decision making (MADM) method that is used to identify the most attractive alternative solution among a finite set of alternatives based on the simultaneous minimization of the distance from an ideal solution (IS) and the maximization of the distance from the nadir solution (NS). We propose an alternative compromise ratio method (CRM) using an efficient and powerful distance measure for solving the group MADM problems. In the proposed CRM, similar to TOPSIS, the chosen alternative should be simultaneously as close as possible to the IS and as far away as possible from the NS. The conventional MADM problems require well-defined and precise data; however, the values associated with the parameters in the real-world are often imprecise, vague, uncertain or incomplete. Fuzzy sets provide a powerful tool for dealing with the ambiguous data. We capture the decision makers’ (DMs’) judgments with linguistic variables and represent their importance weights with fuzzy sets. The fuzzy group MADM (FGMADM) method proposed in this study improves the usability of the CRM. We integrate the FGMADM method into a strengths, weaknesses, opportunities and threats (SWOT) analysis framework to show the applicability of the proposed method in a solar panel manufacturing firm in Canada. | An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis |
S1568494613001415 | Network reliability optimization for multistate flow networks (MFN) is an important issue for many system supervisors. Network reliability maximization for an MFN by determining the optimal component assignment, where a set of multistate components are ready to be assigned to the network, is a common problem. Previous research solved this problem by developing and applying genetic algorithm. Ant colony optimization (ACO) finds a good solution quickly by utilizing the experience of the proceeding ant but sometimes falls into local optimum. Tabu search (TS) adopts a tabu list to avoid searching in the same direction, and thus it explores other possible solutions. This strategy enlarges the search space. Therefore, we propose a hybrid ant-tabu (HAT) algorithm integrating the advantages of ACO and TS to solve this problem, where network reliability is evaluated in terms of minimal paths (MPs) and Recursive Sum of Disjoint Products. Experimental (RSDP) results show that the proposed HAT has better computational efficiency than several soft computing algorithms for networks with more than six MPs or 10 arcs. | A hybrid ant-tabu algorithm for solving a multistate flow network reliability maximization problem |
S1568494613001427 | The complexity of an algorithm is usually specified by the maximum number of steps made by the algorithm, as a function of the size of the input. However, as different inputs of equal size can yield dramatically different algorithm runtime, the size of the input is not always an appropriate basis for predicting algorithm runtime. In this paper, we argue that the compressed size of the input is more appropriate for this purpose. In particular, we devise a genetic algorithm for compressing a graph by finding the most compact description of its structure, and we demonstrate how the compressed size of the problem instance correlates with the runtime of an exact algorithm for two hard combinatorial problems (graph coloring and Boolean satisfiability). | Predicting algorithmic complexity through structure analysis and compression |
S1568494613001439 | The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets. | Evaluation of reservoir sedimentation using data driven techniques |
S1568494613001567 | The successful application of the real-coded differential evolution (DE) to a wide range of real-valued problems has motivated researchers to investigate its potentiality to integer and discrete valued problems. In most of such works, a real-valued solution is converted into a desired integer-valued solution by applying some posterior decoding mechanisms. Only a limited number of works are found, in which attempts are made for developing an actual integer-coded DE. In this article, such a DE is presented which can work directly with real, integer and discrete variables of a problem without any conversion. In the computational experiments carried out with a set of test problems taken from literature, the DE improved several previously known best solutions, as well as outperformed some similar proposals in most of the cases. | A real–integer–discrete-coded differential evolution |
S1568494613001579 | The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems. The problem becomes even more complicated when dynamic power limit based ramp rate constraint is taken into account. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which genetic algorithm (GA) has been investigated widely since long back. Such proposals have been made for solving only the integer part of the UCP, along with some other approaches for the real part of the problem. Moreover, the ramp rate constraint is usually discussed only in the formulation part, without addressing how it could be implemented in an algorithm. In this paper, the GA is revisited with an attempt to solve both the integer and real parts of the UCP using a single algorithm, as well as to incorporate the ramp rate constraint in the proposed algorithm also. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed GA is found quite satisfactory in comparison with the previously reported results. | Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm |
S1568494613001580 | Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequence approach with an enlarged pheromone-particle table, and (4) global best exchange. These hybrid systems were applied to data clustering. The experimental results utilizing public UCI datasets show that the performances of the proposed hybrid systems are superior compared to those of the K-mean, standalone PSO, and standalone ACOR. Among the four strategies of hybridization, the sequence approach with the enlarged pheromone table is superior to the other approaches because the enlarged pheromone table diversifies the generation of new solutions of ACOR and PSO, which prevents traps into the local optimum. | Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering |
S1568494613001592 | An efficient algorithm named Pattern search (PS) has been used widely in various scientific and engineering fields. However, even though the global convergence of PS has been proved, it does not perform well on more complex and higher dimension problems nowadays. In order to improve the efficiency of PS and obtain a more powerful algorithm for global optimization, a new algorithm named Free Pattern Search (FPS) based on PS and Free Search (FS) is proposed in this paper. FPS inherits the global search from FS and the local search from PS. Two operators have been designed for accelerating the convergence speed and keeping the diversity of population. The acceleration operator inspired by FS uses a self-regular management to classify the population into two groups and accelerates all individuals in the first group, while the throw operator is designed to avoid the reduplicative search of population and keep the diversity. In order to verify the performance of FPS, two famous benchmark instances are conducted for the comparisons between FPS with Particle Swarm Optimization (PSO) variants and Differential Evolution (DE) variants. The results show that FPS obtains better solutions and achieves the higher convergence speed than other algorithms. Pattern Search Free Search particle swarm optimization Free Pattern Search Hooke and Jeeves Pattern Search differential evolution direct search simulated annealing differential evolution with biogeography-based optimization directed tabu search cellular particle swarm optimization integrated learning particle swarm optimizer learning-enhanced differential evolution clustering-based differential evolution the pattern matrix the current point in Pattern Search the previous point in Pattern Search the base point in Pattern Search the search step size of pattern the search step sizes factor of the pattern the reduce factor of pattern the population size of Free Pattern Search the number of the dimension the numbers of steps for local Pattern Search the rate of the detection range the jth individual in the population the ith element of an individual | Free Pattern Search for global optimization |
S1568494613001609 | This research discusses the application of a mixed-integer-binary small-population-based evolutionary particle swarm optimization to the problem of optimal power flow, where the optimization problem has been formulated taking into account four decision variables simultaneously: active power (continuous), voltage generator (continuous), tap position on transformers (integer) and shunt devices (binary). The constraint handling technique used in the algorithm is based on a strategy to generate and keep the decision variables in feasible space through the heuristic operators. The heuristic operators are applied in the active power stage and the reactive power stage sequentially. Firstly, the heuristic operator for the power balance is computed in order to maintain the power balance constraint through a re-dispatch of the thermal units. Secondly, the heuristic operators for the limit of active power flows and the bus voltage constraint at each generator bus are executed through the sensitivity factors. The advantage of our approach is that the algorithm focuses the search of the decision variables on the feasible solution space, obtaining a better cost in the objective function. Such operators not only improve the quality of the final solutions but also significantly improve the convergence of the search process. The methodology is verified in several electric power systems. | Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems |
S1568494613001622 | In this paper a new methodology for training radial basis function (RBF) neural networks is introduced and examined. This novel approach, called Fuzzy-OSD, could be used in applications, which need real-time capabilities for retraining neural networks. The proposed method uses fuzzy clustering in order to improve the functionality of the Optimum Steepest Descent (OSD) learning algorithm. This improvement is due to initialization of RBF units more precisely using fuzzy C-Means clustering algorithm that results in producing better and the same network response in different retraining attempts. In addition, adjusting RBF units in the network with great accuracy will result in better performance in fewer train iterations, which is essential when fast retraining of the network is needed, especially in the real-time systems. We employed this new method in an online radar pulse classification system, which needs quick retraining of the network once new unseen emitters detected. Having compared result of applying the new algorithm and Three-Phase OSD method to benchmark problems from Proben1 database and also using them in our system, we achieved improvement in the results as presented in this paper. | Improvement of RBF neural networks using Fuzzy-OSD algorithm in an online radar pulse classification system |
S1568494613001634 | Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input–output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM–CTM), and some of those input–output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM–CTM model only, when compared to the measurement data collected at monitoring stations. | Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels |
S156849461300166X | In this study, a novel approach via the composite of fuzzy controllers and dithers is presented. According to this approach, we can synthesize a set of fuzzy controllers and find appropriate dithers to stabilize nonlinear multiple time-delay (NMTD) interconnected systems. A robustness design of model-based fuzzy control is first proposed to overcome the effect of modeling errors between the NMTD interconnected subsystems and Takagi–Sugeno (T–S) fuzzy models. In terms of Lyapunov's direct method, a delay-dependent stability criterion is then derived to guarantee the asymptotic stability of NMTD interconnected systems. Based on this criterion and the decentralized control scheme, a set of model-based fuzzy controllers is synthesized via the technique of parallel distributed compensation (PDC) to stabilize the NMTD interconnected system. When the designed fuzzy controllers cannot stabilize the NMTD interconnected systems, a batch of high-frequency signals (commonly referred to as dithers) is simultaneously introduced to stabilize it. If the frequencies of dithers are high enough, the outputs of the dithered interconnected system and those of its corresponding mathematical model–the relaxed interconnected system can be made as close as desired. This makes it possible to obtain a rigorous prediction of the stability of the dithered interconnected system based on the one of the relaxed interconnected system. Finally, a numerical example is given to illustrate the feasibility of our approach. | Delay-dependent T–S fuzzy control for nonlinear interconnected systems: Using dithers as auxiliaries |
S1568494613001671 | Exploration and exploitation are omnipresent terms in evolutionary computation community that have been broadly utilized to explain how evolutionary algorithms perform search. However, only recently exploration and exploitation measures were presented in a quantitative way enabling to measure amounts of exploration and exploitation. To move a step further, this paper introduces a parameter control approach that utilizes such measures as feedback to adaptively control evolution processes. The paper shows that with new exploration and exploitation measures, the evolution process generates relatively well results in terms of fitness and/or convergence rate when applying to a practical chemical engineering problem of fitting Sovova's model. We also conducted an objective statistical analysis using Bonferroni–Dunn test and sensitivity analysis on the experimental results. The statistical analysis results again proved that the parameter control strategy using exploration and exploitation measures is competitive to the other approaches presented in the paper. The sensitivity analysis results also showed that different initial values may affect output in different magnitude. | A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model |
S1568494613001683 | The Particle Swarm Optimization (PSO) is a simple, yet very effective, population-based search algorithm. However, degradation of the population diversity in the late stages of the search, or stagnation, is the PSO's major drawback. Most of the related recent research efforts are concentrated on alleviating this drawback. The direct solution to this problem is to introduce modifications which increase exploration; however it is difficult to maintain the balance of exploration and exploitation of the search process with this approach. In this paper we propose the decoupling of exploration and exploitation using a team-oriented search. In the proposed algorithm, the swarm is divided into two independent teams or sub swarms; each dedicated to a particular aspect of search. A simple but effective local search method is proposed for exploitation and an improvised PSO structure is used for exploration. The validation is conducted using a wide variety of benchmark functions which include shifted and rotated versions of popular test functions along with recently proposed composite functions and up to 1000 dimensions. The results show that the proposed algorithm provides higher quality solution with faster convergence and increased robustness compared to most of the recently modified or hybrid algorithms based on PSO. In terms of algorithm complexity, TOSO is slightly more complex than PSO but much less complex than CLPSO. For very high dimensions (D >400), however, TOSO is the least complex compared to both PSO and CLPSO. | A team-oriented approach to particle swarms |
S1568494613001695 | Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions. | A novel artificial bee colony algorithm with Powell's method |
S1568494613001701 | This paper proposes a novel method for real-time trajectory generation of a seven-link planar biped robot. Individual joint trajectories are generated by specifying only two key parameters, walking speed and step length. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation, experimental design, Takagi–Suguno fuzzy systems, central pattern generator (CPG), ZMP criterion and a dynamic balance method. A fuzzy relationship between walking and CPG parameters is learned using experimental design methodology and T–S fuzzy systems. A method based on the Fourier series is used to tune parameters of the CPG. The proposed method allows making online changes to step length and walking speed while ensuring robot's dynamic balance. A multibody simulation package is selected and the effectiveness of the method is illustrated using several examples. It is also shown that changes in joint angles, as the result of online changes to the walking parameters, occur in a smooth and continuous manner. | Online bio-inspired trajectory generation of seven-link biped robot based on T–S fuzzy system |
S1568494613001713 | Based on grey relational analysis, this study attempts to propose a grey evolutionary analysis (GEA) to analyze the population distribution of particle swarm optimization (PSO) during the evolutionary process. Then two GEA-based parameter automation approaches are developed. One is for the inertia weight and the other is for the acceleration coefficients. With the help of the GEA technique, the proposed parameter automation approaches would enable the inertia weight and acceleration coefficients to adapt to the evolutionary state. Such parameter automation behaviour also makes an attempt on the GEA-based PSO to perform a global search over the search space with faster convergence speed. In addition, the proposed PSO is applied to solve the optimization problems of twelve unimodal and multimodal benchmark functions for illustration. Simulation results show that the proposed GEA-based PSO could outperform the adaptive PSO, the grey PSO, and two well-known PSO variants on most of the test functions. | Particle swarm optimization with grey evolutionary analysis |
S1568494613001828 | Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration signal caused by bearing localized defects is easily overwhelmed by other strong vibration signals. Extracting the bearing fault signal from a multi-component signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The filtered signal is then obtained by applying the optimal wavelet filter to the multi-component signal mixture. After that, a new adaptive local maximum selection method is proposed to make the filtered signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component signal mixtures, including a simulated signal and a real vibration signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration signals and noise. | A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture |
S156849461300183X | The ability of artificial neural networks (ANN) to model the rainfall–discharge relationships of karstic aquifers has been studied in the Terminio massif (Southern Italy), which supplies the Naples area with a yearly mean discharge of approximately 1–3.5m3/s. The Mediterranean climate causes a rapid increase in evapotranspiration and a decrease in rainfall towards spring–summer. Especially during drought, and in combination with highly sensitive climatic parameters, there are dramatic changes in the discharge amount especially during the July and August months. A neural network model was developed based on MLP (multi-layer perceptron) network to forecast of water resources three and six month before the main stress months of July and August. Example data were extracted on an ultra-centenarian hydrological serial. The training and validation phases, confirmed by a ten fold cross validation methodology, led to a very satisfactory calibration of the ANN model, with errors in forecasting discharge values of just 5% (three months before) and 10% (six months before). | Three-and-six-month-before forecast of water resources in a karst aquifer in the Terminio massif (Southern Italy) |
S1568494613001841 | This paper deals with the problem of digital IIR filter design. Two novel modifications are proposed to Particle Swarm Optimization and validated through novel application for design of IIR filter. First modification is based on quantum mechanics and proved to yield a better performance. The second modification is to take care of time dependency character of the constriction factor. Extensive simulation results validate the superior performance of proposed algorithms. | Swarm intelligence based techniques for digital filter design |
S1568494613001853 | Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a limitation in common in that recognition errors are likely to be produced when background noise surrounds the spoken command, thereby creating potential dangers for the disabled if recognition errors exist in the control systems. In this paper, a hybrid noise suppression filter is proposed to interface with the commercial speech recognizers in order to enhance the recognition accuracy under variant noisy conditions. It intends to decrease the recognition errors when the commercial speech recognizers are working under a noisy environment. It is based on a sigmoid function which can effectively enhance noisy speech using simple computational operations, while a robust estimator based on an adaptive-network-based fuzzy inference system is used to determine the appropriate operational parameters for the sigmoid function in order to produce effective speech enhancement under variant noisy conditions. The proposed hybrid noise suppression filter has the following advantages for commercial speech recognizers: (i) it is not possible to tune the inbuilt parameters on the commercial speech recognizers in order to obtain better accuracy; (ii) existing noise suppression filters are too complicated to be implemented for real-time speech recognition; and (iii) existing sigmoid function based filters can operate only in a single-noisy condition, but not under varying noisy conditions. The performance of the hybrid noise suppression filter was evaluated by interfacing it with a commercial speech recognizer, commonly used in electronic products. Experimental results show that improvement in terms of recognition accuracy and computational time can be achieved by the hybrid noise suppression filter when the commercial recognizer is working under various noisy environments in factories. | A hybrid noise suppression filter for accuracy enhancement of commercial speech recognizers in varying noisy conditions |
S1568494613001877 | This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines genetic algorithm (GA) and market-based optimal power flow (OPF). The method jointly maximizes net present value (NPV) related to WTs investment made by WTs’ developers and social welfare (SW) considering different combinations of wind generation and load demand over a year. The GA is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis. The effectiveness of the method is demonstrated with an 84-bus 11.4kV radial distribution system. | Optimal wind turbines placement within a distribution market environment |
S1568494613001889 | Characters in video games usually use a manually-defined topology of the environment to navigate. To evolve in an open, unknown and dynamic world, characters should not have pre-existing representations of their environment. In this paper, characters learn this representation by imitating human players. We here put forward a modified version of the growing neural gas model (GNG) called stable growing neural gas (SGNG). The algorithm is able to learn how to use the environment from one or more teachers (players) by representing it with a graph. Unlike GNG, SGNG learning is in-line, reflecting the dynamic nature of the environment. The evaluation of the quality of the learned representations are detailed. | Stable growing neural gas: A topology learning algorithm based on player tracking in video games |
S1568494613001890 | The paper introduces several modifications to self-learning fuzzy spiking neural network that is used as a base for evolving system design. The adaptive wavelet activation-membership functions are utilized to improve and generalize receptive neuron activation functions and the temporal Hebbian learning algorithm. The proposed evolving spiking wavelet-neuro-fuzzy self-learning system retains native features of spiking neurons and reveals evolving systems’ capabilities in detecting overlapping clusters of irregular form. | Evolving spiking wavelet-neuro-fuzzy self-learning system |
S1568494613001920 | Self-organizing fuzzy controllers (SOFCs) have excellent learning capabilities. They have been proposed for the manipulation of active suspension systems. However, it is difficult to select the parameters of an SOFC appropriately, and an SOFC may extensively modify its fuzzy rules during the control process when the parameters selected for it are inappropriate. To eliminate this problem, this study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems. The GPSOFC introduces a grey-prediction algorithm into an SOFC, in order to pre-correct its fuzzy rules for the control of active suspension systems. This design solves the problem of SOFCs with inappropriately chosen parameters. To evaluate the feasibility of the proposed method, this study applied the GPSOFC to the manipulation of an active hydraulic-servo suspension system, in order to determine its control performance. Experimental results demonstrated that the GPSOFC achieved better control performance than either the SOFC or the passive method of active suspension control. | Design of a grey-prediction self-organizing fuzzy controller for active suspension systems |
S1568494613001932 | Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology. | A hybrid expert system approach for telemonitoring of vocal fold pathology |
S1568494613001944 | Evolutionary algorithms (EAs), which have been widely used to solve various scientific and engineering optimization problems, are essentially stochastic search algorithms operating in the overall solution space. However, such random search mechanism may lead to some disadvantages such as a long computing time and premature convergence. In this study, we propose a space search optimization algorithm (SSOA) with accelerated convergence strategies to alleviate the drawbacks of the purely random search mechanism. The overall framework of the SSOA involves three main search mechanisms: local space search, global space search, and opposition-based search. The local space search that aims to form new solutions approaching the local optimum is realized based on the concept of augmented simplex method, which exhibits significant search abilities realized in some local space. The global space search is completed by Cauchy searching, where the approach itself is based on the Cauchy mutation. This operation can help the method avoid of being trapped in local optima and in this way alleviate premature convergence. An opposition-based search is exploited to accelerate the convergence of space search. This operator can effectively reduce a substantial computational overhead encountered in evolutionary algorithms (EAs). With the use of them SSOA realizes an effective search process. To evaluate the performance of the method, the proposed SSOA is contrasted with a method of differential evolution (DE), which is a well-known space concept-based evolutionary algorithm. When tested against benchmark functions, the SSOA exhibits a competitive performance vis-a-vis performance of some other competitive schemes of differential evolution in terms of accuracy and speed of convergence, especially in case of high-dimensional continuous optimization problems. | A space search optimization algorithm with accelerated convergence strategies |
S1568494613001956 | Traditional methods on creating diesel engine models include the analytical methods like multi-zone models and the intelligent based models like artificial neural network (ANN) based models. However, those analytical models require excessive assumptions while those ANN models have many drawbacks such as the tendency to overfitting and the difficulties to determine the optimal network structure. In this paper, several emerging advanced machine learning techniques, including least squares support vector machine (LS-SVM), relevance vector machine (RVM), basic extreme learning machine (ELM) and kernel based ELM, are newly applied to the modelling of diesel engine performance. Experiments were carried out to collect sample data for model training and verification. Limited by the experiment conditions, only 24 sample data sets were acquired, resulting in data scarcity. Six-fold cross-validation is therefore adopted to address this issue. Some of the sample data are also found to suffer from the problem of data exponentiality, where the engine performance output grows up exponentially along the engine speed and engine torque. This seriously deteriorates the prediction accuracy. Thus, logarithmic transformation of dependent variables is utilized to pre-process the data. Besides, a hybrid of leave-one-out cross-validation and Bayesian inference is, for the first time, proposed for the selection of hyperparameters of kernel based ELM. A comparison among the advanced machine learning techniques, along with two traditional types of ANN models, namely back propagation neural network (BPNN) and radial basis function neural network (RBFNN), is conducted. The model evaluation is made based on the time complexity, space complexity, and prediction accuracy. The evaluation results show that kernel based ELM with the logarithmic transformation and hybrid inference is far better than basic ELM, LS-SVM, RVM, BPNN and RBFNN, in terms of prediction accuracy and training time. | Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set |
S1568494613001968 | In this paper, a new hybrid classifier is proposed by combining neural network and direct fractional-linear discriminant analysis (DF-LDA). The proposed hybrid classifier, neural tree with linear discriminant analysis called NTLD, adopts a tree structure containing either a simple perceptron or a linear discriminant at each node. The weakly performing perceptron nodes are replaced with DF-LDA in an automatic way. Taking the advantage of this node substitution, the tree building process converges faster and avoids the over-fitting of complex training sets in training process resulting a shallower tree together with better classification performance. The proposed NTLD algorithm is tested on various synthetic and real datasets. The experimental results show that the proposed NTLD leads to very satisfactory results in terms of tree depth reduction as well as classification accuracy. | Incorporating linear discriminant analysis in neural tree for multidimensional splitting |
S156849461300197X | A semi-integrated system for driver assistance and pedestrian safety is presented. This system is composed of a single camera which focuses on the driver for picking up visual cues and a stereo rig that focus on the road ahead for the detection of road obstructions and pedestrians. While the car is in motion, the driver's viewing direction is obtained and analyzed along with information of road condition and any moving vehicle ahead in order to determine if the current driving condition is safe. In addition, when the vehicle is moving slowly, the system can also detect the existence of a pedestrian ahead and warns the driver if the pedestrian moves in front of the car. This system contains algorithm-based safety analysis as well as fuzzy rules-based analysis for interaction between variables. Our experimental results show that the condition for driver safety can be accurately classified in 94.5% of the tested driving conditions, and the pedestrians can be identified in 93.18% of the tested cases. These were compared to the results of similar systems and shown to be superior. | An integrated driver warning system for driver and pedestrian safety |
S1568494613001981 | Robotic systems have inherently nonlinear phenomena as joints undergo sliding and/or rotating. This in turn requires that the system running states be predicted correctly. This paper makes a full analysis of the robot states by applying observer-based adaptive wavelet neural network (OBAWNN) tracking control scheme to tackle these phenomena such as system uncertainties, multiple time-delayed state uncertainties, and external disturbances such that the closed loop system signals must obey uniform ultimate boundedness and achieve H ∞ tracking performance. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system, while an observer-based adaptive control scheme is to stabilize the system. The advantage of employing adaptive wavelet neural dynamics is that we can utilize the neuron information by activation functions to on-line tune the hidden-to-output weights, and the adaptation parameters to estimate the robot parameters and the bounds of the gains of delay states directly using linear analytical results. It is shown that the stability of the closed-loop system is guaranteed by some sufficient conditions derived from Lyapunov criterion and Riccati-inequality. Finally, a numerical example of a three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme. | An observer-based adaptive neural network tracking control of robotic systems |
S1568494613001993 | Designing oligonucleotide strands that selectively hybridize to reduce undesired reactions is a critical step for successful DNA computing. To accomplish this, DNA molecules must be restricted to a wide window of thermodynamical and logical conditions, which in turn facilitate and control the algorithmic processes implemented by chemical reactions. In this paper, we propose a multiobjective evolutionary algorithm for DNA sequence design that, unlike preceding evolutionary approaches, uses a matrix-based chromosome as encoding strategy. Computational results show that a matrix-based GA along with its specific genetic operators may improve the performance for DNA sequence optimization compared to previous methods. | Improving the design of sequences for DNA computing: A multiobjective evolutionary approach |
S1568494613002007 | This paper presents a Genetic Algorithm for the optimization of multiple indices of Quality of Service of Multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood Multiobjective Genetic Algorithm (VN-MGA), is a Genetic Algorithm based on the NSGA-II, with the particular feature that solutions are encoded defining two different kinds of neighborhoods. The first neighborhood is defined by considering as decision variables the edges that form the routes to be followed by each request, whilst the second part of solution is kept constant. The second neighborhood is defined by considering the request sequence as decision variable, with the first part kept constant. Comparisons are performed with: (i) a VNS algorithm that performs a switch between the same two neighborhoods that are used in VN-MGA; and (ii) the results obtained with an integer linear programming solver, running a scalarized version of the multiobjective problem. The results indicate that the proposed VN-MGA outperforms the pure VNS algorithm, and provides a good approximation of the exact Pareto fronts obtained with Integer Linear Programming (ILP) approach, at a much smaller computational cost. Besides potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms. | Multiobjective optimization of MPLS-IP networks with a variable neighborhood genetic algorithm |
S1568494613002019 | The structure of a neural network is determined by time-consuming trial-and-error tuning procedure in advance for the reason that it is difficult to consider the balance between the neuron number and the desired performance. To attack this problem, a self-evolving functional-linked wavelet neural network (SFWNN) is proposed. Without the need for preliminary knowledge, a self-evolving approach demonstrates that the properties of generating and pruning the hidden neurons automatically. Then, an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) system which is composed of a neural controller and a supervisory compensator is proposed. The neural controller uses a SFWNN to online estimate an ideal controller and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. To investigate the capabilities of the proposed ASFWNC approach, it is applied to a chaotic system and a DC motor. The simulation and experimental results show that favorable control performance can be achieved by the proposed ASFWNC scheme. | A self-evolving functional-linked wavelet neural network for control applications |
S1568494613002020 | The paper presents a novel approach for voice activity detection. The main idea behind the presented approach is to use, next to the likelihood ratio of a statistical model-based voice activity detector, a set of informative distinct features in order to, via a supervised learning approach, enhance the detection performance. The statistical model-based voice activity detector, which is chosen based on the comparison to other similar detectors in an earlier work, models the spectral envelope of the signal and we derive the likelihood ratio thereof. Furthermore, the likelihood ratio together with 70 other various features was meticulously analyzed with an input variable selection algorithm based on partial mutual information. The resulting analysis produced a 13 element reduced input vector which when compared to the full input vector did not undermine the detector performance. The evaluation is performed on a speech corpus consisting of recordings made by six different speakers, which were corrupted with three different types of noises and noise levels. In the end, we tested three different supervised learning algorithms for the task, namely, support vector machine, Boost, and artificial neural networks. The experimental analysis was performed by 10-fold cross-validation due to which threshold averaged receiver operating characteristics curves were constructed. Also, the area under the curve score and Matthew's correlation coefficient were calculated for both the three supervised learning classifiers and the statistical model-based voice activity detector. The results showed that the classifier with the reduced input vector significantly outperformed the standalone detector based on the likelihood ratio, and that among the three classifiers, Boost showed the most consistent performance. | Partial mutual information based input variable selection for supervised learning approaches to voice activity detection |
S1568494613002032 | Assembly lines for mass manufacturing incrementally build production items by performing tasks on them while flowing between workstations. The configuration of an assembly line consists of assigning tasks to different workstations in order to optimize its operation subject to certain constraints such as the precedence relationships between the tasks. The operation of an assembly line can be optimized by minimizing two conflicting objectives, namely the number of workstations and the physical area these require. This configuration problem is an instance of the TSALBP, which is commonly found in the automotive industry. It is a hard combinatorial optimization problem to which finding the optimum solution might be infeasible or even impossible, but finding a good solution is still of great value to managers configuring the line. We adapt eight different Multi-Objective Ant Colony Optimization (MOACO) algorithms and compare their performance on ten well-known problem instances to solve such a complex problem. Experiments under different modalities show that the commonly used heuristic functions deteriorate the performance of the algorithms in time-limited scenarios due to the added computational cost. Moreover, even neglecting such a cost, the algorithms achieve a better performance without such heuristic functions. The algorithms are ranked according to three multi-objective indicators and the differences between the top-4 are further reviewed using statistical significance tests. Additionally, these four best performing MOACO algorithms are favourably compared with the Infeasibility Driven Evolutionary Algorithm (IDEA) designed specifically for industrial optimization problems. | A comparative study of Multi-Objective Ant Colony Optimization algorithms for the Time and Space Assembly Line Balancing Problem |
S1568494613002044 | This work proposes a biogeography and geo-sciences based soft computing technique which is an extension of original biogeography based feature extraction algorithm using the concept of entropy inspired from the geo-sciences phenomenon of mantle convection and dynamics of the earth. This algorithm uses surface entropy in the relevant band of multi-spectral images as the basis of calculating the habitat suitability index which in turn forms the basis of identifying different terrain features in the satellite image. The proposed work has been primarily developed for the purpose of finding the applications of geo-sciences in developing computationally intelligent models. This may lead to another concept of process randomization, generation of virtual scenarios, etc. which are important ingredients in battlefield assessment. The proposed feature extractor algorithm has been applied on the datasets of Alwar region in Rajasthan and Patalganga area in Shivalik ranges. The results indicate that our proposed geo-sciences based classifier is highly efficient in extracting land cover features. Further when integrated with hybrid bio-inspired intelligent classifier proposed in our previous work, it improves its classification efficiency and outperforms the earlier probabilistic classifiers, recent soft computing classifiers such as membrane computing, hybrid FPAB/BBO, extended non-linear BBO, etc. and the very recent hybrid ACO2/PSO/BBO classifier proposed by us [16,21]. Our results conclude that the classifier based on our proposed model is the best known classifier developed till date. The proposed model is flexible and can adapt itself to suit to a large number of classification problems including mixed pixel resolution, face recognition, pattern recognition, etc. whereby entropy can be simply calculated in any other band or according to its standard definition and hence feature extraction can be made. | Biogeography and geo-sciences based land cover feature extraction |
S1568494613002056 | Relevance feedback methods in CBIR (Content Based Image Retrieval) iteratively use relevance information from the user to search the space for other relevant samples. As several regions of interest may be scattered through the space, an effective search algorithm should balance the exploration of the space to find new potential regions of interest and the exploitation of areas around samples which are known relevant. However, many algorithms concentrate the search on areas which are close to the images that the user has marked as relevant, according to a distance function in the (possibly deformed) multidimensional feature space. This maximizes the number of relevant images retrieved at the first iterations, but limits the discovery of new regions of interest and may leave unexplored a large section of the space. In this paper, we propose a novel hybrid approach that uses a scattered search algorithm based on NSGA II (Non-dominated Sorting Genetic Algorithm) only at the first iteration of the relevance feedback process, and then switches to an exploitation algorithm. The combined approach has been tested on three databases and in combination with several other methods. When the hybrid method does not produce better results from the first iteration, it soon catches up and improves both precision and recall. | A hybrid multi-objective optimization algorithm for content based image retrieval |
S1568494613002068 | The study introduces and discusses a principle of justifiable granularity, which supports a coherent way of designing information granules in presence of experimental evidence (either of numerical or granular character). The term “justifiable” pertains to the construction of the information granule, which is formed in such a way that it is (a) highly legitimate (justified) in light of the experimental evidence, and (b) specific enough meaning it comes with a well-articulated semantics (meaning). The design process associates with a well-defined optimization problem with the two requirements of experimental justification and specificity. A series of experiments is provided as well as a number of constructs carried for various formalisms of information granules (intervals, fuzzy sets, rough sets, and shadowed sets) are discussed as well. | Building the fundamentals of granular computing: A principle of justifiable granularity |
S156849461300207X | This paper proposes a method for finding solutions of arbitrarily nonlinear systems of functional equations through stochastic global optimization. The original problem (equation solving) is transformed into a global optimization one by synthesizing objective functions whose global minima, if they exist, are also solutions to the original system. The global minimization task is carried out by the stochastic method known as fuzzy adaptive simulated annealing, triggered from different starting points, aiming at finding as many solutions as possible. To demonstrate the efficiency of the proposed method, solutions for several examples of nonlinear systems are presented and compared with results obtained by other approaches. We consider systems composed of n equations on Euclidean spaces ℝ n (n variables: x 1, x 2, x 3, ⋯, x n ). | Solving nonlinear systems of functional equations with fuzzy adaptive simulated annealing |
S1568494613002081 | In the classical Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), the decision maker (DM) gives the pair-wise comparisons of alternatives with crisp truth degree 0 or 1. However, in the real world, DM is not sure enough in all comparisons and can express his/her opinion with some fuzzy truth degree. Thus, DM's preferences are given through pair-wise comparisons of alternatives with fuzzy truth degrees, which may be represented as trapezoidal fuzzy numbers (TrFNs). Considered such fuzzy truth degrees, the aim of this paper is to develop a new fuzzy linear programming technique for solving multiattribute decision making (MADM) problems with multiple types of attribute values and incomplete weight information. In this method, TrFNs, real numbers, and intervals are used to represent the multiple types of decision information. The fuzzy consistency and inconsistency indices are defined as TrFNs due to the alternatives’ comparisons with fuzzy truth degrees. Hereby a new fuzzy linear programming model is constructed and solved by the possibility linear programming method with TrFNs developed in this paper. The fuzzy ideal solution (IS) and the attribute weights are then obtained. The distances of alternatives from the fuzzy IS can be calculated to determine their ranking order. The implementation process of the method proposed in this paper is illustrated with a strategy partner selection example. The comparison analyzes show that the method proposed in this paper generalizes the classical LINMAP, fuzzy LINMAP and possibility LINMAP. | Fuzzy linear programming approach to multiattribute decision making with multiple types of attribute values and incomplete weight information |
S1568494613002093 | Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase. | A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules |
S156849461300210X | Control of power electronics converters used in PV system is very much essential for the efficient operation of the solar system. In this paper, a modified incremental conduction maximum power point tracking (MPPT) algorithm in conjunction with an adaptive fuzzy controller is proposed to control the DC–DC boost converter in the PV system under rapidly varying atmospheric and partial shading conditions. An adaptive hysteresis current controller is proposed to control the inverter. The proposed current controller provides constant switching frequency with less harmonic content compared with fixed hysteresis current control algorithm and sinusoidal PWM controller. The modeling and simulation of PV system along with the proposed controllers are done using MATLAB/SIMSCAPE software. Simulation results show that the proposed MPPT algorithm is faster in transient state and presents smoother signal with less fluctuations in steady state. The hardware implementation of proposed MPPT algorithm and inverter current control algorithms using Xilinx spartran-3 FPGA is also presented. The experimental results show satisfactory performance of the proposed approaches. | Development and analysis of adaptive fuzzy controllers for photovoltaic system under varying atmospheric and partial shading condition |
S1568494613002111 | Network clustering algorithms are typically based only on the topology information of the network. In this paper, we introduce traffic as a quantity representing the intensity of the relationship among nodes in the network, regardless of their connectivity, and propose an evolutionary clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. In a comparative evaluation based on synthetic instances and two real world datasets, we show that our approach outperforms a selection of well established evolutionary and non-evolutionary clustering algorithms. | A traffic-based evolutionary algorithm for network clustering |
S1568494613002123 | This paper proposes a new Modified Backtracking Ensemble Pruning algorithm (ModEnPBT), which is based upon the design idea of our previously proposed Ensemble Pruning via Backtracking algorithm (EnPBT), and however, aiming at overcoming its drawback of redundant solution space definition. Solution space of ModEnPBT is compact with no repeated solution vectors, therefore it possesses relatively higher searching efficiency compared with EnPBT algorithm. ModEnPBT still belongs to the category of Backtracking algorithm, which can systematically search for the solutions of a problem in a manner of depth-first, which is suitable for solving all those large-scale combinatorial optimization problems. Experimental results on three benchmark classification tasks demonstrate the validity and effectiveness of the proposed ModEnPBT. | ModEnPBT: A Modified Backtracking Ensemble Pruning algorithm |
S1568494613002135 | The initial subsurface flow of whole basin plays a quite important role in daily rainfall–runoff simulation. However, general physically based rainfall–runoff model, such as the XXT model (a hybrid model of TOPographic MODEL and the Xinanjiang model), is difficult to catch the non-linear factors and take full advantages of previous information of rainfall and runoff that is essential to the initial watershed average saturation deficit of each time step. In order to address the issue, this study selected the initial subsurface flow for the whole time series of the XXT model as the breakthrough point, and used the observed runoff and rainfall data of two days before the present day as the inputs of artificial neural network (ANN) and initial subsurface flow of the present day as the output, then integrated ANN into runoff generation module of XXT model and finally tested the integrated model for daily runoff simulation in large-scale and semi-arid Linyi watershed, eastern China. In addition, this work employ particle swarm optimization (PSO) algorithm to seek the best combination of 6 physical parameters in XXT and a great number of weights in ANN to avoid the local optimization. The results show that the integrated model performs much better than XXT in terms of Nash–Sutcliffe efficiency coefficient (NE) and root mean square error (RMSE). Hence, the new integrating approach proposed here is promising for daily rainfall–runoff modeling and can be easily extended to other process-based models. | An innovative method for dynamic update of initial water table in XXT model based on neural network technique |
S1568494613002159 | Natural computing, inspired by biological course of action, is an interdisciplinary field that formalizes processes observed in living organisms to design computational methods for solving complex problems, or designing artificial systems with more natural behaviour. Based on the tasks abstracted from natural phenomena, such as brain modelling, self-organization, self-repetition, self evaluation, Darwinian survival, granulation and perception, nature serves as a source of inspiration for the development of computational tools or systems that are used for solving complex problems. Nature inspired main computing paradigms used for such development include artificial neural networks, fuzzy logic, rough sets, evolutionary algorithms, fractal geometry, DNA computing, artificial life and granular or perception-based computing. Information granulation in granular computing is an inherent characteristic of human thinking and reasoning process performed in everyday life. The present article provides an overview of the significance of natural computing with respect to the granulation-based information processing models, such as neural networks, fuzzy sets and rough sets, and their hybridization. We emphasize on the biological motivation, design principles, application areas, open research problems and challenging issues of these models. | Title Paper: Natural computing: A problem solving paradigm with granular information processing |
S1568494613002287 | This paper presents an evolving ant direction particle swarm optimization algorithm for solving the optimal power flow problem with non-smooth and non-convex generator cost characteristics. In this method, ant colony search is used to find a suitable velocity updating operator for particle swarm optimization and the ant colony parameters are evolved using genetic algorithm approach. To update the velocities for particle swarm optimization, five velocity updating operators are used in this method. The power flow problem is solved by the Newton–Raphson method. The feasibility of the proposed method was tested on IEEE 30-bus, IEEE 39-bus and IEEE-57 bus systems with three different objective functions. Several cases were investigated to test and validate the effectiveness of the proposed method in finding the optimal solution. Simulation results prove that the proposed method provides better results compared to classical particle swarm optimization and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices. | Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis |
S1568494613002299 | This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25h to 36s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value. | Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid |
S1568494613002305 | Variable-rate fertilization (VRF) decision is a key aspect of prescription generation in precision agriculture, which typically involves multiple criteria and objectives. This paper presents a multiobjective optimization problem model for oil crop fertilization, which takes into consideration not only crop yield and quality but also energy consumption and environmental effects. For efficiently solving the problem, we propose a hybrid multiobjective fireworks optimization algorithm (MOFOA) that evolves a set of solutions to the Pareto optimal front by mimicking the explosion of fireworks. In particular, it uses the concept of Pareto dominance for individual evaluation and selection, and combines differential evolution (DE) operators to increase information sharing among the individuals. The experimental tests and real-world applications in oil crop production in east China demonstrate the effectiveness and practicality of the algorithm. the number of fields the number of types of fertilizer the (average) gradient of field i the (average) plant density of field i the unit price of fertilizer j the dosage of fertilizer j in field i the inherent quantity of fertilizer j in field i the residual of fertilizer j in field i the function for estimating the crop yield the function for evaluating the crop quality the function for estimating the cost of fertilization the function for estimating the energy consumption of fertilization the function for estimating the residual fertilizer | Multiobjective fireworks optimization for variable-rate fertilization in oil crop production |
S1568494613002317 | Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model parameters which represent the PV device. In the present work, parameter estimation for the model parameter using various evolutionary algorithms is presented and compared. The constraint set on the estimation process is that only the data directly available in module datasheets can be used for estimating the parameters. The electrical model accuracy using the estimated parameters is then compared to several electrical models reported in literature for various PV cell technologies. modified diode ideality factor (V) diode ideality factor air mass angle of incidence irradiance (W/m2) band-gap energy of PV cell material (eV) fill factor fraction of diffuse radiation absorbed in the module PV module output current (A) light current (A) diode reverse saturation current (A) Boltzmann's constant, 1.38066E−23 (J/K) irradiance dependence parameter for I L temperature dependence parameter for a number of cells in series in a module's cell-string number of cell-strings in parallel in module number of cells in series in a module's cell-string electrical power (W) elementary charge, 1.60218×10−19 (coulomb) series resistance (Ω) shunt resistance (Ω) plane-of-array absorbed solar radiation at operating conditions (W/m2) temperature (°C) voltage (V) Temperature coefficient of maximum power point current temperature coefficient of short circuit current temperature coefficient of open circuit voltage temperature coefficient of maximum power point voltage thermal voltage per cell at temperature Tc temperature coefficient of open circuit voltage overall diode ideality factor of PV module reference cell condition ambient beam radiation PV cell diffuse radiation effective radiation; experimental module back surface; modeled maximum power point open circuit point reference cell condition short circuit point IV point at module voltage equal to half of open circuit voltage IV point at module voltage equal to average of max. power and open circuit voltages ambient | Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms |
S1568494613002329 | This paper addresses a hybrid solution methodology involving modified shuffled frog leaping algorithm (MSFLA) with genetic algorithm (GA) crossover for the economic load dispatch problem of generating units considering the valve-point effects. The MSFLA uses a more dynamic and less stochastic approach to problem solving than classical non-traditional algorithms, such as genetic algorithm, and evolutionary programming. The potentiality of MSFLA includes its simple structure, ease of use, convergence property, quality of solution, and robustness. In order to overcome the defects of shuffled frog leaping algorithm (SFLA), such as slow searching speed in the late evolution and getting trapped easily into local iteration, MSFLA with GA cross-over is put forward in this paper. MSFLA with GA cross-over produces better possibilities of getting the best result in much less global as well as local iteration as one has strong local search capability while the other is good at global search. This paper proposes a new approach for solving economic load dispatch problems with valve-point effect where the cost function of the generating units exhibits non-convex characteristics, as the valve-point effects are modeled and imposed as rectified sinusoid components. The combined methodology and its variants are validated for the following four test systems: IEEE standard 30 bus test system, a practical Eastern Indian power grid system of 203 buses, 264 lines, and 23 generators, and 13 and 40 thermal units systems whose incremental fuel cost function take into account the valve-point loading effects. The results are quite promising and effective compared with several benchmark methods. | Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect |
S1568494613002330 | This study investigated the effects of upstream stations’ flow records on the performance of artificial neural network (ANN) models for predicting daily watershed runoff. As a comparison, a multiple linear regression (MLR) analysis was also examined using various statistical indices. Five streamflow measuring stations on the Cahaba River, Alabama, were selected as case studies. Two different ANN models, multi layer feed forward neural network using Levenberg–Marquardt learning algorithm (LMFF) and radial basis function (RBF), were introduced in this paper. These models were then used to forecast one day ahead streamflows. The correlation analysis was applied for determining the architecture of each ANN model in terms of input variables. Several statistical criteria (RMSE, MAE and coefficient of correlation) were used to check the model accuracy in comparison with the observed data by means of K-fold cross validation method. Additionally, residual analysis was applied for the model results. The comparison results revealed that using upstream records could significantly increase the accuracy of ANN and MLR models in predicting daily stream flows (by around 30%). The comparison of the prediction accuracy of both ANN models (LMFF and RBF) and linear regression method indicated that the ANN approaches were more accurate than the MLR in predicting streamflow dynamics. The LMFF model was able to improve the average of root mean square error (RMSEave) and average of mean absolute percentage error (MAPEave) values of the multiple linear regression forecasts by about 18% and 21%, respectively. In spite of the fact that the RBF model acted better for predicting the highest range of flow rate (flood events, RMSEave/RBF=26.8m3/s vs. RMSEave/LMFF=40.2m3/s), in general, the results suggested that the LMFF method was somehow superior to the RBF method in predicting watershed runoff (RMSE/LMFF=18.8m3/s vs. RMSE/RBF=19.2m3/s). Eventually, statistical differences between measured and predicted medians were evaluated using Mann-Whitney test, and differences in variances were evaluated using the Levene's test. | Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff |
S1568494613002342 | Robust and real-time moving object tracking is a tricky job in computer vision systems. The development of an efficient yet robust object tracker faces several obstacles, namely: dynamic appearance of deformable or articulated targets, dynamic backgrounds, variation in image intensity, and camera (ego) motion. In this paper, a novel tracking algorithm based on particle swarm optimization (PSO) method is proposed. PSO is a population-based stochastic optimization algorithm modeled after the simulation of the social behavior of bird flocks and animal hordes. In this algorithm, a multi-feature model is proposed for object detection to enhance the tracking accuracy and efficiency. The object's model is based on the gray level intensity. This model combines the effects of different object cases including zooming, scaling, rotating, etc. into a single cost function. The proposed algorithm is independent of object type and shape and can be used for many object tracking applications. Over 30 video sequences and having over 20,000 frames are used to test the developed PSO-based object tracking algorithm and compare it to classical object tracking algorithms as well as previously published PSO-based tracking algorithms. Our results demonstrate the efficiency and robustness of our developed algorithm relative to all other tested algorithms. | Efficient multi-feature PSO for fast gray level object-tracking |
S1568494613002354 | Evolutionary computation (EC) paradigm has undergone extensions in the recent years diverging from the natural process of genetic evolution to the simulation of natural life processes exhibited by the living organisms. Bee colonies exemplify a high level of intrinsic interdependence and co-ordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The artificial bee colony (ABC) algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving real-parameter, non-convex, and non-smooth optimization problems. The single parameter perturbation in classical ABC resulted in fairly commendable performance for simple problems without epistasis of variables (separable). However, it suffered from narrow search zone and slow convergence which eventually led to poor exploitation tendency. Even with the increase in dimensionality, a significant deterioration was observed in the ability of ABC to locate the optimum in a huge search volume. Some of the probable shortcomings in the basic ABC approach, as observed, are the single parameter perturbation instead of a multiple one, ignoring the fitness to reward ratio while selecting food sites, and most importantly the absence of environmental factors in the algorithm design. Research has shown that spatial environmental factors play a crucial role in insect locomotion and foragers seem to learn the direction to be undertaken based on the relative analysis of its proximal surroundings. Most importantly, the mapping of the forager locomotion from three dimensional search spaces to a multidimensional solution space calls forth the implementation of multiple modification schemes. Based on the fundamental observation pertaining to the dynamics of ABC, this article proposes an improved variant of ABC aimed at improving the optimizing ability of the algorithm over an extended set of problems. The hybridization of the proposed fitness learning mechanism with a weighted selection scheme and proximity based stimuli helps to achieve a fine blending of explorative and exploitative behaviour by enhancing both local and global searching ability of the algorithm. This enhances the ability of the swarm agents to detect optimal regions in the unexplored fitness basins. With respect to its immediate surroundings, a proximity based component is added to the normal positional modification of the onlookers and is enacted through an improved probability selection scheme that takes the T/E (total reward to distance) ratio metric into account. The biologically-motivated, hybridized variant of ABC achieves a statistically superior performance on majority of the tested benchmark instances, as compared to some of the most prominent state-of-the-art algorithms, as is demonstrated through a detailed experimental evaluation and verified statistically. | Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization |
S1568494613002366 | Protein subcellular localization plays a vital role in understanding proteins’ behavior under different circumstances. The effectiveness of various drugs can be assessed by the successful prediction of protein locations. Therefore, it is important to develop a prediction system that is sufficiently reliable and accurate in making decisions regarding the protein localization. However, main problem in developing a reliable and high throughput prediction system is the presence of imbalanced data, which greatly affects the performance of a prediction system. In order to remedy this problem, we utilized the notion of oversampling through Synthetic Minority Oversampling TEchnique (SMOTE). Further, different feature extraction strategies and ensemble classification techniques are assessed for their contribution toward the solution of the challenging problem of subcellular localization. After applying SMOTE data balancing technique, a remarkable improvement is observed in the performance of random forest and rotation forest ensemble classifiers for CHOM, CHOA and VeroA datasets. It is anticipated that our proposed model might be helpful for the research community in the field of functional and structural proteomics as well as in drug discovery. | Subcellular localization using fluorescence imagery: Utilizing ensemble classification with diverse feature extraction strategies and data balancing |
S1568494613002378 | In this paper, human motion analysis is performed by modeling a physical complex exercise in order to provide feedback about the patient's performance in rehabilitation therapies. The Sun Salutation exercise, which is a flowing sequence of 12 yoga poses, is analyzed. This exercise provides physical benefits as improving the strength and flexibility of the muscles and the alignment of the spinal column. A temporal series of measures that contains a numerical description of this sequence is obtained by using a wearable sensing system for monitoring, which is formed by five high precision tri-axial accelerometer sensors worn by the patient while performing the exercise. Due to the complexity of the exercise and the huge amount of available data, its interpretation is a challenging task. Therefore, this paper describes the design of a computational system able of interpreting and generating linguistic descriptions about this exercise. Previous works on both Granular Linguistic Models of Phenomena and Fuzzy Finite State Machines are used to create a basic linguistic model of the Sun Salutation. This model allows generating human friendly reports focused on the assessment of the exercise quality based on its symmetry, stability and rhythm. | A tool for linguistic assessment of rehabilitation exercises |
S156849461300238X | Analytic Hierarchy Process (AHP) is increasingly applied to healthcare and medical research and applications. However, knowledge representation of pairwise reciprocal matrix is still dubious. This research discusses the related drawbacks, and recommends pairwise opposite matrix as the ideal alternative. Pairwise opposite matrix is the key foundation of Primitive Cognitive Network Process (P-CNP), which revises the AHP approach with practical changes. A medical decision treatment evaluation using AHP is revised by P-CNP with a step-by-step tutorial. Comparisons with AHP have been discussed. The proposed method could be a promising decision tool to replace AHP to share information among patients or/and doctors, and to evaluate therapies, medical treatments, health care technologies, medical resources, and healthcare policies. | The Primitive Cognitive Network Process in healthcare and medical decision making: Comparisons with the Analytic Hierarchy Process |
S1568494613002391 | A hybrid algorithm combining Regrouping Particle Swarm Optimization (RegPSO) with wavelet radial basis function neural network referred to as (RegPSO-WRBF-NN) algorithm is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on clean and polluted high-voltage glass insulators by using surface tracking and erosion test procedure of international electro-technical commission 60,587. A laboratory experiment was conducted by preparing the prototypes of the discharges. A very important step for the WRBF network training is to decide a proper number of hidden nodes, centers, spreads and the network weights can be viewed as a system identification problem. So PSO is used to optimize the WRBF neural network parameters in this work. Therefore, the combination method based on the WRBF neural network is adapted. A regrouping technique called as a Regrouping Particle Swarm Optimization (RegPSO) is also used to help the swarm escape from the state of premature convergence, RegPSO was able to solve the stagnation problem for the surface discharge dataset tested and approximate the true global minimizer. Testing results indicate that the proposed approach can make a quick response and yield accurate solutions as soon as the inputs are given. Comparisons of learning performance are made to the existing conventional networks. This learning method has proven to be effective by applying the wavelet radial basis function based on the RegPSO neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and revealed a very high classification rate. | Hybrid regrouping PSO based wavelet neural networks for characterization of acoustic signals due to surface discharges on H.V. glass insulators |
S1568494613002524 | This article addresses the problem of dynamic job scheduling on a single machine with Poisson arrivals, stochastic processing times and due dates, in the presence of sequence-dependent setups. The objectives of minimizing mean earliness and mean tardiness are considered. Two approaches for dynamic scheduling are proposed, a Reinforcement Learning-based and one based on Fuzzy Logic and multi-objective evolutionary optimization. The performance of the two scheduling approaches is tested against the performance of 15 dispatching rules in four simulation scenarios with different workload and due date pressure conditions. The scheduling methods are compared in terms of Pareto optimal-oriented metrics, as well as in terms of minimizing mean earliness and mean tardiness independently. The experimental results demonstrate the merits of the proposed methods. | Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups |
S1568494613002615 | In wireless multimedia sensor networks (WMSNs), sensor nodes use different types of sensors to gather different types of data. In multimedia applications, it is necessary to provide reliable and fair protocols in order to meet specific requirements of quality of service (QoS) demands in regard to these different types of data. To prolong the system lifetime of WMSNs, it is necessary to perform adjustments to the transmission rate and to mitigate network congestion. In previous works investigating WMSNs, exponential weighted priority-based rate control (EWPBRC) schemes with traffic load parameter (TLP) schemes in WMSNs were used to control congestion by adjusting transmission rates relative to various data types. However, when the TLP is fixed, a large change in data transmission causes a significant difference between input transmission rate and the estimated output transmission rate of each sensor node. This study proposes a novel fuzzy logical controller (FLC) pertaining to TLP schemes with an EWPBRC that estimates the output transmission rate of the parent node and then assigns a suitable transmission rate based on the traffic load of each child node, with attention paid to the different amounts of data being transmitted. Simulation results show that the performance of our proposed scheme has a better transmission rate as compared to PBRC: the delay and loss probability are reduced. In addition, our proposed scheme can effectively control different transmission data types insofar as achieving the QoS requirements of a system while decreasing network resource consumption. | A fuzzy logical controller for traffic load parameter with priority-based rate in wireless multimedia sensor networks |
S1568494613002627 | In this paper, we present boosted SVM dedicated to solve imbalanced data problems. Proposed solution combines the benefits of using ensemble classifiers for uneven data together with cost-sensitive support vectors machines. Further, we present oracle-based approach for extracting decision rules from the boosted SVM. In the next step we examine the quality of the proposed method by comparing the performance with other algorithms which deal with imbalanced data. Finally, boosted SVM is used for medical application of predicting post-operative life expectancy in the lung cancer patients. | Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients |
S1568494613002639 | Liver transplantation is nowadays a widely-accepted treatment for patients who present a terminal liver disease. Nevertheless, transplantation is greatly hampered by the un-availability of suitable liver donors; several methods have been developed and applied to find a better system to prioritize recipients on the waiting list, although most of them only consider donor or recipient characteristics (but not both). This paper proposes a novel donor–recipient liver allocation system constructed to predict graft survival after transplantation by means of a dataset comprised of donor–recipient pairs from different centres (seven Spanish and one UK hospitals). The best model obtained is used in conjunction with the Model for End-stage Liver Disease score (MELD), one of the current assignation methodology most used globally. This problem is assessed using the ordinal regression learning paradigm due to the natural ordering in the classes of the problem, via a cascade binary decomposition methodology and the Support Vector Machine methodology. The methodology proposed has shown competitiveness in all the metrics selected, when compared to other machine learning techniques and efficiently complements the MELD score based on the principles of efficiency and equity. Finally, a simulation of the proposal is included, in order to visualize its performance in realistic situations. This simulation has shown that there are some determining factors in the characterization of the survival time after transplantation (concerning both donors and recipients) and that the joint use of these sets of information could be, in fact, more useful and beneficial for the survival principle. Nonetheless, the results obtained indicate the true complexity of the problem dealt within this study and the fact that other characteristics that have not been included in the dataset may be of importance for the characterization of the dependent variable (survival time after transplantation), thus starting a promising line of future work. | An organ allocation system for liver transplantation based on ordinal regression |
S1568494613002640 | Combinatorial problems like flow shop scheduling, travel salesman problem etc. get complicated and are difficult to solve when the problem size increases. To overcome this problem, we present a block-based evolutionary algorithm (BBEA) which will conduct evolutionary operations on a set of blocks instead of genes. BBEA includes the block mining and block recombination approaches. A block mining algorithm is developed to decompose a chromosome into a set of blocks and rest of genes. The block is with a fixed length and can be treated as a building block in forming a new chromosome later on. To guide the block mining process, a gene linkage probability matrix is defined that shows the linkage strength among genes. Therefore the blocks can be further evolved during the evolutionary processes using this matrix. In the block recombination approach, the blocks along with the rest of genes are recombined to form a new chromosome. This new evolutionary approach of BBEA is tested on a set of discrete problems. Experimental results show that BBEA is very competitive when compared with traditional GA, EA or ACGA and HGIA approaches and it can largely improve the performance of evolutionary algorithm and save a fair amount of computational times simultaneously. | A block-based evolutionary algorithm for flow-shop scheduling problem |
S1568494613002652 | Flower identification and recognition are tedious and difficult tasks even for humans. Image segmentation based on automatic flower extraction is an essential step for computer-aided flower image recognition and retrieval processes. Furthermore, there is a challenge for segmentation of the object(s) from natural complex background in color images. In this study, a novel performance optimization approach for image segmentation, i.e. simulated annealing-based mean-shift segmentation (SAMS), is proposed and implemented. It is based on the simulated annealing solution of quadratic assignment problem model treated as an image segmentation process using feature-based mean-shift (MS) clustering on color images. The proposed approach is designed to realize a global and unsupervised (i.e., fully automatic) segmentation. It is a modified and optimized version of Backprojection-based mean-shift segmentation (BackMS) method. In conducted segmentation experiments, the performance results of SAMS approach are compared with the ones of BackMS method. Comparison of overall performance results and statistical analysis (i.e., Wilcoxon signed rank median test) show that SAMS approach improves the performance of BackMS method. It is measured as 49.33% when using object bounding boxes and as 51.33% when using object pixel regions. | An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation |
S1568494613002664 | In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations. | Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model |
S1568494613002676 | Satellites situated in the orbit of Mars have provided and continue providing thousand of images of the planet surface. Nevertheless the number of expert geologists analyzing these images is limited. Typically, these experts provide linguistic descriptions of their observations remarking the relevant features in the image and ignoring the irrelevant details for a given goal. In this paper, we apply our research in the field of Computational Theory of Perceptions to the challenge of developing computational systems able to generate linguistic reports comparable with the ones provided by human experts. We present a description of our contribution to solve this problem including last results of our research in this field. For example, we explore how to represent the multidimensional domain of computational perception values. We develop up the use of the relevance as an attribute of perceptions that allows us to generate reports that are automatically suited according to the user goals. We provide an application example as a demonstration of concept. | Linguistic description about circular structures of the Mars’ surface |
S1568494613002688 | State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae. In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization. It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm. Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova. | Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits |
S156849461300269X | Missing data in large insurance datasets affects the learning and classification accuracies in predictive modelling. Insurance datasets will continue to increase in size as more variables are added to aid in managing client risk and will therefore be even more vulnerable to missing data. This paper proposes a hybrid multi-layered artificial immune system and genetic algorithm for partial imputation of missing data in datasets with numerous variables. The multi-layered artificial immune system creates and stores antibodies that bind to and annihilate an antigen. The genetic algorithm optimises the learning process of a stimulated antibody. The evaluation of the imputation is performed using the RIPPER, k-nearest neighbour, naïve Bayes and logistic discriminant classifiers. The effect of the imputation on the classifiers is compared with that of the mean/mode and hot deck imputation methods. The results demonstrate that when missing data imputation is performed using the proposed hybrid method, the classification improves and the robustness to the amount of missing data is increased relative to the mean/mode method for data missing completely at random (MCAR) missing at random (MAR), and not missing at random (NMAR).The imputation performance is similar to or marginally better than that of the hot deck imputation. | Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm |
S1568494613002706 | The three-state test (3ST) – a new approach for chaos detection in discrete chaotic maps is presented. The scheme is based on statistical analyses of patterns obtained from ascending sorting of the system states. In addition to its ability for clear discernment between chaotic, quasi-periodic and periodic dynamical systems, the detection of periods of stable cycles is also automated with 3ST. The method is directly applied on data series generated by chaotic maps and does not require a priori knowledge of the equations of the underlying system. It also presents the advantage of not having to depend on the nature of the vector field as well as its dimensionality and is computationally low cost. The effectiveness of the 3ST is confirmed using two well known and widely studied chaotic maps: the logistic map and the Henon 2D map. | The three-state test for chaos detection in discrete maps |
S1568494613002718 | The best performing methods for Dynamic Optimization Problems (DOPs) are usually based on a set of agents that can have different complexity (like solutions in Evolutionary Algorithms, particles in Particle Swarm Optimization, or metaheuristics in hybrid cooperative strategies). While methods based on low-complexity agents are widely applied in DOPs, the use of more “intelligent” agents has rarely been explored. This work focuses on this topic and more specifically on the use of cooperative strategies composed by trajectory-based search agents for DOPs. Within this context, we analyze the influence of the number of agents (cardinality) and their neighborhood sampling strategy on the performance of these methods. Using a low number of agents with distinct neighborhood sampling strategies shows the best results. This method is then compared versus state-of-the-art algorithms using as test bed the well-known Moving Peaks Benchmark and dynamic versions of the Ackley's, Griewank's and Rastrigin's functions. The results show that this configuration of the cooperative strategy is competitive with respect to the state-of-the-art methods. | The role of cardinality and neighborhood sampling strategy in agent-based cooperative strategies for Dynamic Optimization Problems |
S156849461300272X | In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA–PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA–PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA–PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to −50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA–PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided. | Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems |
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