AADE

AADE (Auto adaptive differential evolution algorithm)

class DETAlgs.aade.AADE(params: AADEData, db_conn=None, db_auto_write=False)[source]

Bases: BaseAlg

Links: https://ieeexplore.ieee.org/document/8819749

References: V. Sharma, S. Agarwal and P. K. Verma, “Auto Adaptive Differential Evolution Algorithm,” 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 958-963, doi: 10.1109/ICCMC.2019.8819749.

next_epoch()[source]

run()

write_results_to_database(results_data)

class detpy.DETAlgs.data.alg_data.AADEData(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)[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'

mutation_factor: float = 0.1

parallel_processing: list | None = None

population_size: int = 100

ub: list