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Particle Swarm Optimization with Double Learning Patterns

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristi...

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Detalles Bibliográficos
Autores principales: Shen, Yuanxia, Wei, Linna, Zeng, Chuanhua, Chen, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707022/
https://www.ncbi.nlm.nih.gov/pubmed/26858747
http://dx.doi.org/10.1155/2016/6510303
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author Shen, Yuanxia
Wei, Linna
Zeng, Chuanhua
Chen, Jian
author_facet Shen, Yuanxia
Wei, Linna
Zeng, Chuanhua
Chen, Jian
author_sort Shen, Yuanxia
collection PubMed
description Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.
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spelling pubmed-47070222016-02-08 Particle Swarm Optimization with Double Learning Patterns Shen, Yuanxia Wei, Linna Zeng, Chuanhua Chen, Jian Comput Intell Neurosci Research Article Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. Hindawi Publishing Corporation 2016 2015-12-27 /pmc/articles/PMC4707022/ /pubmed/26858747 http://dx.doi.org/10.1155/2016/6510303 Text en Copyright © 2016 Yuanxia Shen 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
Shen, Yuanxia
Wei, Linna
Zeng, Chuanhua
Chen, Jian
Particle Swarm Optimization with Double Learning Patterns
title Particle Swarm Optimization with Double Learning Patterns
title_full Particle Swarm Optimization with Double Learning Patterns
title_fullStr Particle Swarm Optimization with Double Learning Patterns
title_full_unstemmed Particle Swarm Optimization with Double Learning Patterns
title_short Particle Swarm Optimization with Double Learning Patterns
title_sort particle swarm optimization with double learning patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707022/
https://www.ncbi.nlm.nih.gov/pubmed/26858747
http://dx.doi.org/10.1155/2016/6510303
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