EPSRDE

EPSRDE (Differential evolution random locations)

class DETAlgs.eps_rde.EPSRDE(params: EPSRDEData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

EPSRDE - Epsilon Constrained Rank-Based Differential Evolution

Links: https://ieeexplore.ieee.org/document/6256111

References: Tetsuyuki Takahama and Setsuko Sakai “Efficient Constrained Optimization by the ε Constrained Rank-Based Differential Evolution”, 2012 IEEE Congress on Evolutionary Computation, 10-15 June 2012, Brisbane, QLD, Australia doi: 10.1109/CEC.2012.6256111.

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.EPSRDEData(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, crossing_type: detpy.models.enums.crossingtype.CrossingType = <CrossingType.EXPOTENTIAL: 'exponential'>, min_mutation_factor: float = 0.6, max_mutation_factor: float = 0.95, min_crossover_rate: float = 0.85, max_crossover_rate: float = 0.95, penalty_power: int = 2, control_generations: int = 150, epsilon_scaling_factor: int = 5, theta: int = None, 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'

control_generations: int = 150

crossing_type: CrossingType = 'exponential'

dimension: int = 10

epsilon_scaling_factor: int = 5

function: FitnessFunctionBase = None

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

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

lb: list

log_population: bool = False

max_crossover_rate: float = 0.95

max_mutation_factor: float = 0.95

max_nfe: int = 100000

min_crossover_rate: float = 0.85

min_mutation_factor: float = 0.6

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

penalty_power: int = 2

population_size: int = 100

show_plots: bool = True

theta: int = None

tolerance_h: float = 0.001

ub: list