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A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy
In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8926491/ https://www.ncbi.nlm.nih.gov/pubmed/35310587 http://dx.doi.org/10.1155/2022/1522096 |
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author | Yang, Meilan Liu, Yanmin Yang, Jie |
author_facet | Yang, Meilan Liu, Yanmin Yang, Jie |
author_sort | Yang, Meilan |
collection | PubMed |
description | In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we use a central control strategy to update the external archive, so that the archive solution gets a good distribution. When the dominance of the current particle and the individual best particle cannot be determined, to enhance the diversity of the population, the combination method of the current particle and the individual best particle can be used to update the individual best particle. The experimental results show that CCHMOPSO is better than four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms. It is a feasible method for solving multi-objective optimization problems. |
format | Online Article Text |
id | pubmed-8926491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89264912022-03-17 A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy Yang, Meilan Liu, Yanmin Yang, Jie Comput Intell Neurosci Research Article In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we use a central control strategy to update the external archive, so that the archive solution gets a good distribution. When the dominance of the current particle and the individual best particle cannot be determined, to enhance the diversity of the population, the combination method of the current particle and the individual best particle can be used to update the individual best particle. The experimental results show that CCHMOPSO is better than four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms. It is a feasible method for solving multi-objective optimization problems. Hindawi 2022-03-09 /pmc/articles/PMC8926491/ /pubmed/35310587 http://dx.doi.org/10.1155/2022/1522096 Text en Copyright © 2022 Meilan Yang 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 Yang, Meilan Liu, Yanmin Yang, Jie A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy |
title | A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy |
title_full | A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy |
title_fullStr | A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy |
title_full_unstemmed | A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy |
title_short | A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy |
title_sort | hybrid multi-objective particle swarm optimization with central control strategy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8926491/ https://www.ncbi.nlm.nih.gov/pubmed/35310587 http://dx.doi.org/10.1155/2022/1522096 |
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