LSHADE_EPSIN

LSHADE_EPSIN (L-SHADE with Ensemble Sinusoidal Parameter Adaptation)

class DETAlgs.lshade_epsin.LShadeEpsin(params: LShadeEpsinData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/document/7744163

References: Awad, N. H., Ali, M. Z., Suganthan, P. N., & Reynolds, R. G. (2016). An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 2958–2965). 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE. https://doi.org/10.1109/cec.2016.7744163

next_epoch()[source]: Perform the next epoch of the LSHADE-EpSin algorithm.

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

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.LShadeEpsinData(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, minimum_population_size: int = 5, memory_size: int = 5, best_member_percentage: float = 0.2, f_sin_freq: float = 0.1, population_reduction_strategy: detpy.DETAlgs.population_reduction.population_size_reduction_strategy.PopulationSizeReductionStrategy = <detpy.DETAlgs.population_reduction.linear_population_size_reduction.LinearPopulationSizeReduction object at 0x7f136519f560>)[source]

Bases: BaseData

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

best_member_percentage: float = 0.2

boundary_constraints_fun: BoundaryFixing = 'random'

dimension: int = 10

f_sin_freq: float = 0.1

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

max_nfe: int = 100000

memory_size: int = 5

minimum_population_size: int = 5

optimization_type: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_reduction_strategy: PopulationSizeReductionStrategy = <detpy.DETAlgs.population_reduction.linear_population_size_reduction.LinearPopulationSizeReduction object>

population_size: int = 100

show_plots: bool = True

ub: list