src.environment.problem.MOO.MOO_synthetic.zdt_torch

Module Contents

Classes

ZDT_Torch

Introduction

A PyTorch version of the ZDT test suite for multi-objective optimization problems.

ZDT1_Torch

ZDT2_Torch

ZDT3_Torch

ZDT4_Torch

ZDT5_Torch

ZDT6_Torch

ZeroToOneNormalization

Functions

find_non_dominated_indices

Introduction

Find the indices of non-dominated solutions in a population.

A solution is said to be non-dominated if no other solution in the population dominates it. This function performs a pairwise comparison between all solutions.

normalize

API

src.environment.problem.MOO.MOO_synthetic.zdt_torch.find_non_dominated_indices(Point)[source]

Introduction

Find the indices of non-dominated solutions in a population.

A solution is said to be non-dominated if no other solution in the population dominates it. This function performs a pairwise comparison between all solutions.

Args:

  • Point (np.ndarray): A 2D array of shape (n_points, n_objectives), where each row represents the objective values of a solution.

Returns:

  • non_dominated_indices (np.ndarray): Indices of the non-dominated solutions.

class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch(n_var=30, **kwargs)[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

Introduction

A PyTorch version of the ZDT test suite for multi-objective optimization problems.

Initialization

__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT1_Torch(n_var=30, **kwargs)[source]

Bases: src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch

func(x, *args, **kwargs)[source]
get_ref_set(n_ref_points=1000)[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT2_Torch(n_var=30, **kwargs)[source]

Bases: src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch

func(x, *args, **kwargs)[source]
get_ref_set(n_ref_points=1000)[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT3_Torch(n_var=30, **kwargs)[source]

Bases: src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch

func(x, *args, **kwargs)[source]
get_ref_set(n_ref_points=1000)[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT4_Torch(n_var=10)[source]

Bases: src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch

func(x, *args, **kwargs)[source]
get_ref_set(n_ref_points=1000)[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT5_Torch(m=11, n=5, normalize=True, **kwargs)[source]

Bases: src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch

func(x, *args, **kwargs)[source]
get_ref_set(n_ref_points=1000)[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT6_Torch(n_var=10, **kwargs)[source]

Bases: src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZDT_Torch

func(x, *args, **kwargs)[source]
get_ref_set(n_ref_points=1000)[source]
class src.environment.problem.MOO.MOO_synthetic.zdt_torch.ZeroToOneNormalization(lb=None, ub=None)[source]

Initialization

forward(X)[source]
src.environment.problem.MOO.MOO_synthetic.zdt_torch.normalize(X, lb=None, ub=None, return_bounds=False, estimate_bounds_if_none=True)[source]