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  5. GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization

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Preprint
English
2023

GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization

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0 Files

English
2023
arXiv (Cornell University)
DOI: 10.48550/arxiv.2312.07034

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Amir Gandomi
Amir Gandomi

University of Techology Sdyney

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Amir Gandomi
Danial Yazdani
Mohammad Nabi Omidvar
+1 more

Abstract

This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG). These instances cover a broad spectrum of problem features, including varying degrees of modality, ruggedness, symmetry, conditioning, variable interaction structures, basin linearity, and deceptiveness. Purposefully designed, this test suite offers varying difficulty levels and problem characteristics, facilitating rigorous evaluation and comparative analysis of optimization algorithms. By presenting these problems, we aim to provide researchers with a structured platform to assess the strengths and weaknesses of their algorithms against challenges with known, controlled characteristics. For reproducibility, the MATLAB source code for this test suite is publicly available.

How to cite this publication

Amir Gandomi, Danial Yazdani, Mohammad Nabi Omidvar, Kalyanmoy Deb (2023). GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization. arXiv (Cornell University), DOI: 10.48550/arxiv.2312.07034.

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Publication Details

Type

Preprint

Year

2023

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

arXiv (Cornell University)

DOI

10.48550/arxiv.2312.07034

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