src.environment.problem.MOO.MOO_synthetic.uf_torch

Module Contents

Classes

UF1_Torch

Introduction

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

UF2_Torch

UF3_Torch

UF4_Torch

UF5_Torch

UF6_Torch

UF7_Torch

UF8_Torch

UF9_Torch

UF10_Torch

Functions

crtup

Introduction

Generates a set of uniformly distributed reference points (weight vectors) for a given number of objectives. This function is typically used in multi-objective optimization algorithms such as NSGA-III and RVEA, where reference points are required to guide the selection process.

API

src.environment.problem.MOO.MOO_synthetic.uf_torch.crtup(n_obj, n_ref_points=1000)[source]

Introduction

Generates a set of uniformly distributed reference points (weight vectors) for a given number of objectives. This function is typically used in multi-objective optimization algorithms such as NSGA-III and RVEA, where reference points are required to guide the selection process.

Args:

  • n_obj (int): Number of objectives (i.e., the dimensionality of the reference points).

  • n_ref_points (int): Approximate number of desired reference points (default: 1000).

Returns:

  • W (np.ndarray): A 2D array of shape (n_comb, n_obj) representing the generated reference vectors.

  • n_comb (int): Actual number of generated reference vectors.

class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF1_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

Introduction

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

Initialization

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF2_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF3_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF4_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF5_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF6_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF7_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF8_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF9_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]
class src.environment.problem.MOO.MOO_synthetic.uf_torch.UF10_Torch[source]

Bases: src.environment.problem.basic_problem.Basic_Problem_Torch

func(x)[source]
get_ref_set(n_ref_points=1000)[source]
__str__()[source]