Glowworm Swarm Optimization
Example:
from bejoor.swarm_based import GlowwormSwarmOptimization
import math
def rastrigin_function(sol):
n = len(sol)
return 10 * n + sum(x**2 - 10 * math.cos(2 * math.pi * x) for x in sol)
solution_vector = [{"type": "float", "lower_bound": -5.12, "upper_bound": 5.12}] * 7
gso = GlowwormSwarmOptimization(objective_function=rastrigin_function, solution_vector_size=7,
solution_vector=solution_vector, optimization_side="min",
luciferin_decay=0.4, luciferin_enhancement=0.6, sensing_range=3.0, neighbor_count=5,
step_size=0.02, population_size=50, epochs=100)
gso.run()
print(f'Best Global Objective Value: {gso.global_best_objective_value}')
print(f'Best Global Solution: {gso.global_best_solution}')
Parameters:
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objective_function: Objective function needs to be optimized.
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solution_vector_size: Vector size of the candidate solutions.
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solution_vector: A vector which determines the types of each variable in solution vectors.
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optimization_side: Determines maximize or minimize the objective function.
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target_objective_value: Target Objective value.
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target_objective_lower_bound: Target Objective lower bound.
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target_objective_upper_bound: Target Objective upper bound.
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population_size: Number of individuals in the population.
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epochs: Number of generations to run the algorithm.
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luciferin_decay: Decay rate of luciferin (light intensity).
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luciferin_enhancement: Enhancement rate of luciferin after objective evaluation.
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sensing_range: The radius within which glowworms sense each other.
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neighbor_count: Maximum number of neighbors a glowworm can consider.
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step_size: The step size used to move towards neighbors.