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Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems
High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600174/ https://www.ncbi.nlm.nih.gov/pubmed/37880214 http://dx.doi.org/10.1038/s41598-023-43748-w |
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author | Guo, Jia Zhou, Guoyuan Yan, Ke Sato, Yuji Di, Yi |
author_facet | Guo, Jia Zhou, Guoyuan Yan, Ke Sato, Yuji Di, Yi |
author_sort | Guo, Jia |
collection | PubMed |
description | High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges. |
format | Online Article Text |
id | pubmed-10600174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106001742023-10-27 Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems Guo, Jia Zhou, Guoyuan Yan, Ke Sato, Yuji Di, Yi Sci Rep Article High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges. Nature Publishing Group UK 2023-10-25 /pmc/articles/PMC10600174/ /pubmed/37880214 http://dx.doi.org/10.1038/s41598-023-43748-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Guo, Jia Zhou, Guoyuan Yan, Ke Sato, Yuji Di, Yi Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
title | Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
title_full | Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
title_fullStr | Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
title_full_unstemmed | Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
title_short | Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
title_sort | pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600174/ https://www.ncbi.nlm.nih.gov/pubmed/37880214 http://dx.doi.org/10.1038/s41598-023-43748-w |
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