DEGL

DEGL (Differential evolution with neighborhood-based mutation)

class DETAlgs.degl.DEGL(params: DEGLData, db_conn=None, db_auto_write=False)

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/document/5089881/

References: S. Das, A. Abraham, U. K. Chakraborty and A. Konar, “Differential Evolution Using a Neighborhood-Based Mutation Operator,” in IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526-553, June 2009, doi: 10.1109/TEVC.2008.2009457.

next_epoch()

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.DEGLData(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, mutation_factor: float = 0.1, crossover_rate: float = 0.1, radius: int = 10, weight: float = 0.1)

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'

mutation_factor: float = 0.1

parallel_processing: list | None = None

population_size: int = 100

radius: int = 10

ub: list

weight: float = 0.1