Cargando…

Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy

With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventi...

Descripción completa

Detalles Bibliográficos
Autores principales: Xie, Zixuan, Huang, Xueyu, Liu, Wenwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890841/
https://www.ncbi.nlm.nih.gov/pubmed/35251160
http://dx.doi.org/10.1155/2022/9599417
_version_ 1784661734202540032
author Xie, Zixuan
Huang, Xueyu
Liu, Wenwen
author_facet Xie, Zixuan
Huang, Xueyu
Liu, Wenwen
author_sort Xie, Zixuan
collection PubMed
description With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to find the global optimal solution quickly and accurately. In order to deal with these defects of standard particle swarm optimization, this paper proposes a particle swarm optimization algorithm (SHMPSO) based on the hybrid strategy of seed swarm optimization (using codes available from https://gitee.com/mr-xie123234/code/tree/master/). In SHMPSO, a subpopulation coevolution particle swarm optimization algorithm is adopted. In SHMPSO, an elastic candidate-based strategy is used to find a candidate and realize information sharing and coevolution among populations. The mean dimension learning strategy can be used to make the population converge faster and improve the solution accuracy of SHMPSO. Twenty-one benchmark functions and six industries-recognized particle swarm optimization variants are used to verify the advantages of SHMPSO. The experimental results show that SHMPSO has good convergence speed and good robustness and can obtain high-precision solutions.
format Online
Article
Text
id pubmed-8890841
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-88908412022-03-03 Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy Xie, Zixuan Huang, Xueyu Liu, Wenwen Comput Intell Neurosci Research Article With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to find the global optimal solution quickly and accurately. In order to deal with these defects of standard particle swarm optimization, this paper proposes a particle swarm optimization algorithm (SHMPSO) based on the hybrid strategy of seed swarm optimization (using codes available from https://gitee.com/mr-xie123234/code/tree/master/). In SHMPSO, a subpopulation coevolution particle swarm optimization algorithm is adopted. In SHMPSO, an elastic candidate-based strategy is used to find a candidate and realize information sharing and coevolution among populations. The mean dimension learning strategy can be used to make the population converge faster and improve the solution accuracy of SHMPSO. Twenty-one benchmark functions and six industries-recognized particle swarm optimization variants are used to verify the advantages of SHMPSO. The experimental results show that SHMPSO has good convergence speed and good robustness and can obtain high-precision solutions. Hindawi 2022-02-23 /pmc/articles/PMC8890841/ /pubmed/35251160 http://dx.doi.org/10.1155/2022/9599417 Text en Copyright © 2022 Zixuan Xie et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Xie, Zixuan
Huang, Xueyu
Liu, Wenwen
Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
title Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
title_full Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
title_fullStr Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
title_full_unstemmed Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
title_short Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
title_sort subpopulation particle swarm optimization with a hybrid mutation strategy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890841/
https://www.ncbi.nlm.nih.gov/pubmed/35251160
http://dx.doi.org/10.1155/2022/9599417
work_keys_str_mv AT xiezixuan subpopulationparticleswarmoptimizationwithahybridmutationstrategy
AT huangxueyu subpopulationparticleswarmoptimizationwithahybridmutationstrategy
AT liuwenwen subpopulationparticleswarmoptimizationwithahybridmutationstrategy