EIDE

EIDE (An efficient improved differential evolution algorithm)

class DETAlgs.eide.EIDE(params: EIDEData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/document/6390324

References: Z. Dexuan and G. Liqun, “An efficient improved differential evolution algorithm,” Proceedings of the 31st Chinese Control Conference, Hefei, China, 2012, pp. 2385-2390.

next_epoch()[source]

property nfe: int: Number of function evaluations performed so far.

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.EIDEData(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, crossover_rate_min: float = 0.1, crossover_rate_max: float = 0.1, crossing_type: detpy.models.enums.crossingtype.CrossingType = <CrossingType.BINOMIAL: 'binomial'>, y: int = 1, base_vector_schema: detpy.models.enums.basevectorschema.BaseVectorSchema = <BaseVectorSchema.CURRENT: 'current'>)[source]

Bases: BaseData

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

base_vector_schema: BaseVectorSchema = 'current'

boundary_constraints_fun: BoundaryFixing = 'random'

crossing_type: CrossingType = 'binomial'

crossover_rate_max: float = 0.1

crossover_rate_min: float = 0.1

dimension: int = 10

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

max_nfe: int = 100000

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

show_plots: bool = True

ub: list

y: int = 1