JADE

JADE (Adaptive differential evolution with optional external archive)

class DETAlgs.jade.JADE(params: JADEData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/document/5208221

References: J. Zhang and A. C. Sanderson, “JADE: Adaptive Differential Evolution With Optional External Archive,” in IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945-958, Oct. 2009, doi: 10.1109/TEVC.2009.2014613.

next_epoch()

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.JADEData(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, archive_size: int = 10, mutation_factor_mean: float = 0.1, mutation_factor_std: float = 0.1, crossover_rate_mean: float = 0.1, crossover_rate_std: float = 0.1, crossover_rate_low: float = 0.1, crossover_rate_high: float = 0.1, c: float = 0.1, p: float = 0.1)

Bases: BaseData

archive_size: int = 10

boundary_constraints_fun: BoundaryFixing = 'random'

c: float = 0.1

crossover_rate_high: float = 0.1

crossover_rate_low: float = 0.1

crossover_rate_mean: float = 0.1

crossover_rate_std: float = 0.1

dimension: int = 10

epoch: int = 100

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

mode: OptimizationType = 'minimization'

mutation_factor_mean: float = 0.1

mutation_factor_std: float = 0.1

p: float = 0.1

parallel_processing: list | None = None

population_size: int = 100

ub: list