LSHADE_RSP

LSHADE_RSP (Algorithm with a Rank-based Selective Pressure Strategy)

class DETAlgs.lshadersp.LSHADERSP(params: LSHADERSPData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

L-SHADE-RSP: LSHADE Algorithm with a Rank-based Selective Pressure Strategy (RSP)

Links: https://www.scitepress.org/papers/2018/68525/68525.pdf

References: Shakhnaz Akhmedova, Vladimir Stanovov and Eugene Semenkin (2018). Research on In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018) - Volume 1, pages 149-155

next_epoch()[source]: Perform the next epoch of the L-SHADE-RSP algorithm.

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

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.LSHADERSPData(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, scaling_factor_for_rank_selection: float = 3.5, memory_size: int = 5, minimum_population_size: int = 20, population_reduction_strategy: detpy.DETAlgs.population_reduction.population_size_reduction_strategy.PopulationSizeReductionStrategy = <detpy.DETAlgs.population_reduction.linear_population_size_reduction.LinearPopulationSizeReduction object at 0x7f13680add00>)[source]

Bases: BaseData

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

boundary_constraints_fun: BoundaryFixing = 'random'

dimension: int = 10

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

max_nfe: int = 100000

memory_size: int = 5

minimum_population_size: int = 20

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

scaling_factor_for_rank_selection: float = 3.5

show_plots: bool = True

ub: list