EPSDE

EPSDE (Epsilon Constrained Differential Evolution)

class DETAlgs.eps_de.EPSDE(params: EPSDEData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

EPSDE - Epsilon Constrained Differential Evolution

Links: https://ieeexplore.ieee.org/abstract/document/4274215

References: Tetsuyuki Takahama; Setsuko Sakai; Noriyuki Iwane “Solving Nonlinear Constrained Optimization Problems by the Epsilon Constrained Differential Evolution”, 2006 IEEE International Conference on Systems, Man and Cybernetics, 08-11 October 2006, Taipei,Taiwan doi: 10.1109/ICSMC.2006.385209.

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.EPSDEData(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 = 0.7, crossover_rate: float = 0.9, epsilon_level: int = 0, penalty_power: int = 2, tolerance_h: float = 0.001, g_funcs: list[typing.Callable[[list[float]], float]] = <factory>, h_funcs: list[typing.Callable[[list[float]], float]] = <factory>)

Bases: BaseData

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

boundary_constraints_fun: BoundaryFixing = 'random'

crossover_rate: float = 0.9

dimension: int = 10

epsilon_level: int = 0

function: FitnessFunctionBase = None

g_funcs: list[Callable[[list[float]], float]]

h_funcs: list[Callable[[list[float]], float]]

lb: list

log_population: bool = False

max_nfe: int = 100000

mutation_factor: float = 0.7

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

penalty_power: int = 2

population_size: int = 100

show_plots: bool = True

tolerance_h: float = 0.001

ub: list