SHADE

SHADE (Success-History Based Parameter Adaptation for Differential Evolution)

class DETAlgs.shade.SHADE(params: ShadeData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

SHADE: Success-History based Adaptive Differential Evolution

Links: https://ieeexplore.ieee.org/document/6557555

References: Tanabe, R., & Fukunaga, A. (2013). Success-history based parameter adaptation for Differential Evolution. In 2013 IEEE Congress on Evolutionary Computation (pp. 71–78). 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE. https://doi.org/10.1109/cec.2013.6557555

next_epoch()

Perform the next epoch of the SHADE algorithm.

property nfe: int

Number of function evaluations performed so far.

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.ShadeData(population_size: int = 100, max_nfe: int = 100000, dimension: int = 10, additional_stop_criteria: detpy.models.stop_condition.stop_condition.StopCondition = <detpy.models.stop_condition.never_stop_condition.NeverStopCondition object at 0x7f136577ee40>, lb: list = <factory>, ub: list = <factory>, optimization_type: 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, show_plots: bool = True, memory_size: int = 5)

Bases: BaseData

additional_stop_criteria: StopCondition = <detpy.models.stop_condition.never_stop_condition.NeverStopCondition object>

boundary_constraints_fun: BoundaryFixing = 'random'

dimension: int = 10

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

max_nfe: int = 100000

memory_size: int = 5

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

show_plots: bool = True

ub: list