MGDE

MGDE (A many-objective guided differential evolution)

class DETAlgs.mgde.MGDE(params: MGDEData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://link.springer.com/article/10.1007/s10479-022-04641-3

References: Zouache, D., Ben Abdelaziz, F. MGDE: a many-objective guided differential evolution with strengthened dominance relation and bi-goal evolution. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-04641-3

next_epoch()[source]

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.MGDEData(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, crossover_rate: float = 0.1, mutation_factor_f: float = 0.1, mutation_factor_k: float = 0.1, threshold: float = 0.1, mu: float = 0.1)[source]

Bases: BaseData

boundary_constraints_fun: BoundaryFixing = 'random'

crossover_rate: float = 0.1

dimension: int = 10

epoch: int = 100

function: FitnessFunctionBase = None

lb: list

log_population: bool = False

mode: OptimizationType = 'minimization'

mu: float = 0.1

mutation_factor_f: float = 0.1

mutation_factor_k: float = 0.1

parallel_processing: list | None = None

population_size: int = 100

threshold: float = 0.1

ub: list