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A novel hybrid PSO based on levy flight and wavelet mutation for global optimization

The concise concept and good optimization performance are the advantages of particle swarm optimization algorithm (PSO), which makes it widely used in many fields. However, when solving complex multimodal optimization problems, it is easy to fall into early convergence. The rapid loss of population...

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Detalles Bibliográficos
Autores principales: Gao, Yong, Zhang, Hao, Duan, Yingying, Zhang, Huaifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821455/
https://www.ncbi.nlm.nih.gov/pubmed/36608029
http://dx.doi.org/10.1371/journal.pone.0279572
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author Gao, Yong
Zhang, Hao
Duan, Yingying
Zhang, Huaifeng
author_facet Gao, Yong
Zhang, Hao
Duan, Yingying
Zhang, Huaifeng
author_sort Gao, Yong
collection PubMed
description The concise concept and good optimization performance are the advantages of particle swarm optimization algorithm (PSO), which makes it widely used in many fields. However, when solving complex multimodal optimization problems, it is easy to fall into early convergence. The rapid loss of population diversity is one of the important reasons why the PSO algorithm falls into early convergence. For this reason, this paper attempts to combine the PSO algorithm with wavelet theory and levy flight theory to propose a new hybrid algorithm called PSOLFWM. It applies the random wandering of levy flight and the mutation operation of wavelet theory to enhance the population diversity and seeking performance of the PSO to make it search more efficiently in the solution space to obtain higher quality solutions. A series of classical test functions and 19 optimization algorithms proposed in recent years are used to evaluate the optimization performance accuracy of the proposed method. The experimental results show that the proposed algorithm is superior to the comparison method in terms of convergence speed and convergence accuracy. The success of the high-dimensional function test and dynamic shift performance test further verifies that the proposed algorithm has higher search stability and anti-interference performance than the comparison algorithm. More importantly, both t-Test and Wilcoxon’s rank sum test statistical analyses were carried out. The results show that there are significant differences between the proposed algorithm and other comparison algorithms at the significance level α = 0.05, and the performance is better than other comparison algorithms.
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spelling pubmed-98214552023-01-07 A novel hybrid PSO based on levy flight and wavelet mutation for global optimization Gao, Yong Zhang, Hao Duan, Yingying Zhang, Huaifeng PLoS One Research Article The concise concept and good optimization performance are the advantages of particle swarm optimization algorithm (PSO), which makes it widely used in many fields. However, when solving complex multimodal optimization problems, it is easy to fall into early convergence. The rapid loss of population diversity is one of the important reasons why the PSO algorithm falls into early convergence. For this reason, this paper attempts to combine the PSO algorithm with wavelet theory and levy flight theory to propose a new hybrid algorithm called PSOLFWM. It applies the random wandering of levy flight and the mutation operation of wavelet theory to enhance the population diversity and seeking performance of the PSO to make it search more efficiently in the solution space to obtain higher quality solutions. A series of classical test functions and 19 optimization algorithms proposed in recent years are used to evaluate the optimization performance accuracy of the proposed method. The experimental results show that the proposed algorithm is superior to the comparison method in terms of convergence speed and convergence accuracy. The success of the high-dimensional function test and dynamic shift performance test further verifies that the proposed algorithm has higher search stability and anti-interference performance than the comparison algorithm. More importantly, both t-Test and Wilcoxon’s rank sum test statistical analyses were carried out. The results show that there are significant differences between the proposed algorithm and other comparison algorithms at the significance level α = 0.05, and the performance is better than other comparison algorithms. Public Library of Science 2023-01-06 /pmc/articles/PMC9821455/ /pubmed/36608029 http://dx.doi.org/10.1371/journal.pone.0279572 Text en © 2023 Gao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gao, Yong
Zhang, Hao
Duan, Yingying
Zhang, Huaifeng
A novel hybrid PSO based on levy flight and wavelet mutation for global optimization
title A novel hybrid PSO based on levy flight and wavelet mutation for global optimization
title_full A novel hybrid PSO based on levy flight and wavelet mutation for global optimization
title_fullStr A novel hybrid PSO based on levy flight and wavelet mutation for global optimization
title_full_unstemmed A novel hybrid PSO based on levy flight and wavelet mutation for global optimization
title_short A novel hybrid PSO based on levy flight and wavelet mutation for global optimization
title_sort novel hybrid pso based on levy flight and wavelet mutation for global optimization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821455/
https://www.ncbi.nlm.nih.gov/pubmed/36608029
http://dx.doi.org/10.1371/journal.pone.0279572
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