Electromagnetic Field Optimization
Example:
from bejoor.physics_based import ElectromagneticFieldOptimization
def sphere_function(sol):
return sum(x**2 for x in sol)
solution_vector = [{"type": "float", "lower_bound": -5.12, "upper_bound": 5.12}] * 7
efo = ElectromagneticFieldOptimization(objective_function=sphere_function, solution_vector_size=7,
solution_vector=solution_vector, optimization_side="min",
charge_intensity=1, population_size=30, epochs=50)
efo.run()
print(f'Best Global Objective Value: {efo.global_best_objective_value}')
print(f'Best Global Solution: {efo.global_best_solution}')
Parameters:
-
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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charge_intensity: Intensity of the electrical charge.
BibTeX citation to the algorithm
@article{abedinpourshotorban2016electromagnetic,
title={Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm},
author={Abedinpourshotorban, Hosein and Shamsuddin, Siti Mariyam and Beheshti, Zahra and Jawawi, Dayang NA},
journal={Swarm and Evolutionary Computation},
volume={26},
pages={8--22},
year={2016},
publisher={Elsevier}
}