NMDE

NMDE (Novel modified differential evolution algorithm)

class DETAlgs.nmde.NMDE(params: NMDEData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

Links: https://www.sciencedirect.com/science/article/pii/S0898122111000460

References: Dexuan Zou, Haikuan Liu, Liqun Gao, and Steven Li. 2011. A novel modified differential evolution algorithm for constrained optimization problems. Comput. Math. Appl. 61, 6 (March, 2011), 1608–1623. https://doi.org/10.1016/j.camwa.2011.01.029

next_epoch()

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.NMDEData(epoch: int = 100, population_size: int = 100, dimension: int = 10, lb: list = <factory>, ub: list = <factory>, mode: 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, delta_f: float = 0.1, delta_cr: float = 0.1, sp: int = 10)

Bases: BaseData

boundary_constraints_fun: BoundaryFixing = 'random'

delta_cr: float = 0.1

delta_f: float = 0.1

dimension: int = 10

epoch: int = 100

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

mode: OptimizationType = 'minimization'

parallel_processing: list | None = None

population_size: int = 100

sp: int = 10

ub: list