Sine Cosine Algorithm
Example:
from bejoor.population_based import SineCosineAlgorithm
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
csa = SineCosineAlgorithm(objective_function=rastrigin_function, solution_vector_size=7,
solution_vector=solution_vector, optimization_side="min",
a_max=2.0, a_min=0.0, population_size=30, epochs=50)
csa.run()
print(f'Best Global Objective Value: {csa.global_best_objective_value}')
print(f'Best Global Solution: {csa.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.
-
a_max
: Maximum value of the control parameter 'a' (controls the exploration).
-
a_min
: Minimum value of the control parameter 'a' (controls the exploitation).