SPS_LSHADE_EIG

SPS_LSHADE_EIG (Self-Optimizing L-SHADE with Eigenvector Crossover)

class DETAlgs.sps_lshade_eig.SPS_LSHADE_EIG(params: SPSLShadeEIGDATA, db_conn=None, db_auto_write=False)

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/document/7256999

References: Guo, S.-M., Tsai, J. S.-H., Yang, C.-C., & Hsu, P.-H. (2015). A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 1003–1010). 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE. https://doi.org/10.1109/cec.2015.7256999

next_epoch()

Perform the next epoch of the SPS-L-SHADE-EIG algorithm.

property nfe: int

Number of function evaluations performed so far.

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.SPSLShadeEIGDATA(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, population_reduction_strategy: detpy.DETAlgs.population_reduction.population_size_reduction_strategy.PopulationSizeReductionStrategy = <detpy.DETAlgs.population_reduction.linear_population_size_reduction.LinearPopulationSizeReduction object at 0x7f136519f710>, memory_size: int = 20, q: int = 64, f_init: float = 0.5, cr_init: float = 0.3, er_init: float = 1.0, cr_min: float = 0.6, cr_max: float = 0.95, learning_rate_init: float = 0.1, p_best_fraction: float = 0.1, w_ext: float = 1.9, w_er: float = 0.6807, w_cr: float = 0.2079, w_f: float = 0.353)

Bases: BaseData

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

boundary_constraints_fun: BoundaryFixing = 'random'

cr_init: float = 0.3

cr_max: float = 0.95

cr_min: float = 0.6

dimension: int = 10

er_init: float = 1.0

f_init: float = 0.5

function: FitnessFunctionBase = None

lb: list

learning_rate_init: float = 0.1

log_population: bool = False

max_nfe: int = 100000

memory_size: int = 20

minimum_population_size: int = 5

optimization_type: OptimizationType = 'minimization'

p_best_fraction: float = 0.1

parallel_processing: list | None = None

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

population_size: int = 100

q: int = 64

show_plots: bool = True

ub: list

w_cr: float = 0.2079

w_er: float = 0.6807

w_ext: float = 1.9

w_f: float = 0.353