DERL

DERL (Differential evolution random locations)

class DETAlgs.derl.DERL(params: DERLData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://www.sciencedirect.com/science/article/pii/S037722170500281X

References: Kaelo, P. & Ali, Montaz. (2006). A numerical study of some modified differential evolution algorithms. European Journal of Operational Research. 169. 1176-1184. 10.1016/j.ejor.2004.08.047.

next_epoch()[source]

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.DERLData(epoch: int = 100, population_size: int = 100, dimension: int = 10, lb: list = <factory>, ub: list = <factory>, mode: detpy.models.enums.optimization.OptimizationType = <OptimizationType.MINIMIZATION: 'minimization'>, boundary_constraints_fun: detpy.models.enums.boundary_constrain.BoundaryFixing = <BoundaryFixing.RANDOM: 'random'>, function: detpy.models.fitness_function.FitnessFunctionBase = None, log_population: bool = False, parallel_processing: Optional[list] = None, mutation_factor: float = 0.1, crossover_rate: float = 0.1)[source]

Bases: BaseData

boundary_constraints_fun: BoundaryFixing = 'random'

crossover_rate: float = 0.1

dimension: int = 10

epoch: int = 100

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

mode: OptimizationType = 'minimization'

mutation_factor: float = 0.1

parallel_processing: list | None = None

population_size: int = 100

ub: list