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A novel multi-agent simulation based particle swarm optimization algorithm
Recently, there has been considerable research on combining multi-agent simulation and particle swarm optimization in practice. However, most existing studies are limited to specific engineering fields or problems without summarizing a general and universal combination framework. Moreover, particle...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560124/ https://www.ncbi.nlm.nih.gov/pubmed/36227927 http://dx.doi.org/10.1371/journal.pone.0275849 |
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author | Du, Shuhan Fan, Wenhui Liu, Yi |
author_facet | Du, Shuhan Fan, Wenhui Liu, Yi |
author_sort | Du, Shuhan |
collection | PubMed |
description | Recently, there has been considerable research on combining multi-agent simulation and particle swarm optimization in practice. However, most existing studies are limited to specific engineering fields or problems without summarizing a general and universal combination framework. Moreover, particle swarm optimization can be less effective in complex problems due to its weakness in balancing exploration and exploitation. Yet, it is not common to combine multi-agent simulation with improved versions of the algorithm. Therefore, this paper proposes an improved particle swarm optimization algorithm, introducing a multi-level structure and a competition mechanism to enhance exploration while balancing exploitation. The performance of the algorithm is tested by a set of comparison experiments. The results have verified its capability of converging to high-quality solutions at a fast rate while holding the swarm diversity. Further, a problem-independent simulation-optimization approach is proposed, which integrates the improved algorithm into multi-agent systems, aiming to simulate realistic scenarios dynamically and solve related optimization problems simultaneously. The approach is implemented in a response planning system to find optimal arrangements for response operations after the Sanchi oil spill accident. Results of the case study suggest that compared with the commonly-used shortest distance selection method, the proposed approach significantly shortens the overall response time, improves response efficiency, and mitigates environmental pollution. |
format | Online Article Text |
id | pubmed-9560124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95601242022-10-14 A novel multi-agent simulation based particle swarm optimization algorithm Du, Shuhan Fan, Wenhui Liu, Yi PLoS One Research Article Recently, there has been considerable research on combining multi-agent simulation and particle swarm optimization in practice. However, most existing studies are limited to specific engineering fields or problems without summarizing a general and universal combination framework. Moreover, particle swarm optimization can be less effective in complex problems due to its weakness in balancing exploration and exploitation. Yet, it is not common to combine multi-agent simulation with improved versions of the algorithm. Therefore, this paper proposes an improved particle swarm optimization algorithm, introducing a multi-level structure and a competition mechanism to enhance exploration while balancing exploitation. The performance of the algorithm is tested by a set of comparison experiments. The results have verified its capability of converging to high-quality solutions at a fast rate while holding the swarm diversity. Further, a problem-independent simulation-optimization approach is proposed, which integrates the improved algorithm into multi-agent systems, aiming to simulate realistic scenarios dynamically and solve related optimization problems simultaneously. The approach is implemented in a response planning system to find optimal arrangements for response operations after the Sanchi oil spill accident. Results of the case study suggest that compared with the commonly-used shortest distance selection method, the proposed approach significantly shortens the overall response time, improves response efficiency, and mitigates environmental pollution. Public Library of Science 2022-10-13 /pmc/articles/PMC9560124/ /pubmed/36227927 http://dx.doi.org/10.1371/journal.pone.0275849 Text en © 2022 Du 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 Du, Shuhan Fan, Wenhui Liu, Yi A novel multi-agent simulation based particle swarm optimization algorithm |
title | A novel multi-agent simulation based particle swarm optimization algorithm |
title_full | A novel multi-agent simulation based particle swarm optimization algorithm |
title_fullStr | A novel multi-agent simulation based particle swarm optimization algorithm |
title_full_unstemmed | A novel multi-agent simulation based particle swarm optimization algorithm |
title_short | A novel multi-agent simulation based particle swarm optimization algorithm |
title_sort | novel multi-agent simulation based particle swarm optimization algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560124/ https://www.ncbi.nlm.nih.gov/pubmed/36227927 http://dx.doi.org/10.1371/journal.pone.0275849 |
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