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import numpy as np
import time
# Rastrigin function
def rastrigin_function(x):
A = 10
return A * len(x) + np.sum(x**2 - A * np.cos(2 * np.pi * x))
# Initialize control parameters
SN = 10000 # Number of food sources
MCN = 100000 # Maximum number of cycles
limit = 50 # Maximum number of exploitations for a solution
dimensionality = 2 # Dimensionality of the search space
# Shared variables
food_sources = np.random.uniform(-5.12, 5.12, size=(SN, dimensionality)) # Initial random positions
trial = np.zeros(SN) # Initialize trial counters
# Main ABC loop
start_time = time.time()
for cyc in range(1, MCN + 1):
# Employed Bees' Phase
for i in range(SN):
x_hat = food_sources[i] + np.random.uniform(-0.5, 0.5, size=(dimensionality,))
if rastrigin_function(x_hat) < rastrigin_function(food_sources[i]):
food_sources[i] = x_hat
trial[i] = 0
else:
trial[i] += 1
# Onlooker Bees' Phase
probabilities = 1 / (1 + np.exp(-trial))
onlooker_indices = np.random.choice(SN, size=SN, p=probabilities / probabilities.sum())
for i in onlooker_indices:
x_hat = food_sources[i] + np.random.uniform(-0.5, 0.5, size=(dimensionality,))
if rastrigin_function(x_hat) < rastrigin_function(food_sources[i]):
food_sources[i] = x_hat
trial[i] = 0
else:
trial[i] += 1
# Scout Bee Phase
max_trial_index = np.argmax(trial)
if trial[max_trial_index] > limit:
food_sources[max_trial_index] = np.random.uniform(-5.12, 5.12, size=(dimensionality,))
trial[max_trial_index] = 0
end_time = time.time()
# Find the best solution
best_solution = food_sources[np.argmin([rastrigin_function(x) for x in food_sources])]
print("Best solution:", best_solution)
print("Objective function value at best solution:", rastrigin_function(best_solution))
print("Time taken:", end_time - start_time, "seconds")
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