Gravitational Search Algorithm

Example:


from bejoor.physics_based import GravitationalSearchAlgorithm

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

gsa = GravitationalSearchAlgorithm(objective_function=sphere_function, solution_vector_size=7,
                           solution_vector=solution_vector, optimization_side="min",
                           G_initial=100, G_decay=0.99, population_size=30, epochs=50)
gsa.run()

print(f'Best Global Objective Value: {gsa.global_best_objective_value}')
print(f'Best Global Solution: {gsa.global_best_solution}')

Parameters:

objective_function: Objective function needs to be optimized.
solution_vector_size: Vector size of the candidate solutions.
solution_vector: A vector which determines the types of each variable in solution vectors.
optimization_side: Determines maximize or minimize the objective function.
target_objective_value: Target Objective value.
target_objective_lower_bound: Target Objective lower bound.
target_objective_upper_bound: Target Objective upper bound.
population_size: Number of individuals in the population.
epochs: Number of generations to run the algorithm.
G_initial: Initial value of gravitational constant.
G_decay: Decay rate of gravitational constant per iteration.

BibTeX citation to the algorithm


@article{rashedi2009gsa,
  title={GSA: a gravitational search algorithm},
  author={Rashedi, Esmat and Nezamabadi-Pour, Hossein and Saryazdi, Saeid},
  journal={Information sciences},
  volume={179},
  number={13},
  pages={2232--2248},
  year={2009},
  publisher={Elsevier}
}

Bejoor

Gravitational Search Algorithm

Example:

Parameters:

BibTeX citation to the algorithm

More useful resources about the algorithm: