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Get Free AccessThis 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.
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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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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