SADE

SADE (Self-adaptive differential evolution)

class DETAlgs.sade.SADE(params: SADEData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/abstract/document/4730987

References: Wu Zhi-Feng, Huang Hou-Kuan, Yang Bei and Zhang Ying, “A modified differential evolution algorithm with self-adaptive control parameters,” 2008 3rd International Conference on Intelligent System and Knowledge Engineering , Xiamen, 2008, pp. 524-527, doi: 10.1109/ISKE.2008.4730987.

next_epoch()[source]

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.SADEData(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, prob_f: float = 0.1, prob_cr: float = 0.1)[source]

Bases: BaseData

boundary_constraints_fun: BoundaryFixing = 'random'

dimension: int = 10

epoch: int = 100

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

mode: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

prob_cr: float = 0.1

prob_f: float = 0.1

ub: list