src.environment.problem.SOO.CEC2013LSGO.cec2013lsgo_dataset

Problem Difficulty Classification

Difficulty Mode	Training Set	Testing Set
easy	1, 2, 3, 4, 5, 6, 7, 8, 9	10, 11, 12, 13, 14, 15
difficult	7, 8, 9, 10, 11, 12, 13, 14, 15	1, 2, 3, 4, 5, 6

Note: Functions 7, 8, 9 appear in both easy and difficult categories. When difficulty is ‘all’, both sets contain all problems (1-15).

Module Contents

Classes

CEC2013LSGO_Dataset

Introduction CEC2013LSGO proposes 15 large-scale benchmark problems to represent a wider range of realworld large-scale optimization problems.

API

class src.environment.problem.SOO.CEC2013LSGO.cec2013lsgo_dataset.CEC2013LSGO_Dataset(data, batch_size=1)

Bases: torch.utils.data.Dataset

Introduction

CEC2013LSGO proposes 15 large-scale benchmark problems to represent a wider range of realworld large-scale optimization problems.

Original paper

“Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization.” gene 7.33 (2013): 8.

Official Implementation

CEC2013LSGO

License

GPL-3.0

Problem Suite Composition

CEC2013LSGO contains four major categories of large-scale problems:

1. Fully-separable functions (F1-F3)

2. Two types of partially separable functions:

   1. Partially separable functions with a set of non-separable subcomponents and one fully-separable subcomponents (F4-F7)

   2. Partially separable functions with only a set of non-separable subcomponents and no fullyseparable subcomponent (F8-F11)

3. Two types of overlapping functions:

   1. Overlapping functions with conforming subcomponents (F12-F13)

   2. Overlapping functions with conflicting subcomponents (F14)

4. Fully-nonseparable functions (F15)

Initialization

static get_datasets(version='numpy', train_batch_size=1, test_batch_size=1, difficulty=None, user_train_list=None, user_test_list=None)

Introduction

Generates training and testing datasets for the CEC2013 LSGO benchmark suite based on specified difficulty or user-defined function lists.

Args:

• version (str, optional): Specifies the implementation version to use for function instances. Accepts ‘numpy’ or any other string for alternative (e.g., ‘torch’). Defaults to ‘numpy’.

• train_batch_size (int, optional): Batch size for the training dataset. Defaults to 1.

• test_batch_size (int, optional): Batch size for the testing dataset. Defaults to 1.

• difficulty (str, optional): Difficulty level for dataset split. Accepts ‘easy’, ‘difficult’, ‘all’, or None. If None, user_train_list and user_test_list must be provided.

• user_train_list (list of int, optional): List of function IDs to include in the training set. Used if difficulty is None.

• user_test_list (list of int, optional): List of function IDs to include in the testing set. Used if difficulty is None.

Returns:

• tuple: A tuple containing two CEC2013LSGO_Dataset objects:

  • The first is the training dataset.

  • The second is the testing dataset.

Raises:

• ValueError: If neither difficulty nor both user_train_list and user_test_list are provided.

• ValueError: If an invalid difficulty value is specified.

__getitem__(item)

__len__()

__add__(other: src.environment.problem.SOO.CEC2013LSGO.cec2013lsgo_dataset.CEC2013LSGO_Dataset)

shuffle()