ImprovedDE

ImprovedDE (DE with dynamic mutation parameters)

class DETAlgs.improved_de.ImprovedDE(params: ImprovedDEData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

Links: https://link.springer.com/article/10.1007/s00500-023-09080-1

References: Yifeng Lin · Yuer Yang · Yinyan Zhang Improved differential evolution with dynamic mutation parameters Soft Computing Optimization Published: 17 August 2023 Volume 27, pages 17923–17941, (2023) https://doi.org/10.1007/s00500-023-09080-1

dynamic_mutation_factor(iteration)

Implements dynamic mutation factor based on Scheme 6: FS(k) = 1 - 1 / (1 + exp(-iteration))

next_epoch()

property nfe: int

Number of function evaluations performed so far.

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.ImprovedDEData(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, mutation_factor: float = 0.1, crossover_rate: float = 0.5)

Bases: BaseData

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

boundary_constraints_fun: BoundaryFixing = 'random'

crossover_rate: float = 0.5

dimension: int = 10

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

max_nfe: int = 100000

mutation_factor: float = 0.1

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

show_plots: bool = True

ub: list