A multi-sample particle swarm optimization algorithm based on electric field force

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The pa...

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Autores principales: Shangbo Zhou, Yuxiao Han, Long Sha, Shufang Zhu
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Lenguaje:EN
Publicado: AIMS Press 2021
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spelling oai:doaj.org-article:6e0f60631c6049218a578eb980cb41302021-11-23T02:13:21ZA multi-sample particle swarm optimization algorithm based on electric field force10.3934/mbe.20213691551-0018https://doaj.org/article/6e0f60631c6049218a578eb980cb41302021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021369?viewType=HTMLhttps://doaj.org/toc/1551-0018Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.Shangbo ZhouYuxiao HanLong ShaShufang ZhuAIMS Pressarticleparticle swarm optimizationelectric field forcecomprehensive learningsegmented learningparameter adaptationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7464-7489 (2021)
institution DOAJ
collection DOAJ
language EN
topic particle swarm optimization
electric field force
comprehensive learning
segmented learning
parameter adaptation
Biotechnology
TP248.13-248.65
Mathematics
QA1-939
spellingShingle particle swarm optimization
electric field force
comprehensive learning
segmented learning
parameter adaptation
Biotechnology
TP248.13-248.65
Mathematics
QA1-939
Shangbo Zhou
Yuxiao Han
Long Sha
Shufang Zhu
A multi-sample particle swarm optimization algorithm based on electric field force
description Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.
format article
author Shangbo Zhou
Yuxiao Han
Long Sha
Shufang Zhu
author_facet Shangbo Zhou
Yuxiao Han
Long Sha
Shufang Zhu
author_sort Shangbo Zhou
title A multi-sample particle swarm optimization algorithm based on electric field force
title_short A multi-sample particle swarm optimization algorithm based on electric field force
title_full A multi-sample particle swarm optimization algorithm based on electric field force
title_fullStr A multi-sample particle swarm optimization algorithm based on electric field force
title_full_unstemmed A multi-sample particle swarm optimization algorithm based on electric field force
title_sort multi-sample particle swarm optimization algorithm based on electric field force
publisher AIMS Press
publishDate 2021
url https://doaj.org/article/6e0f60631c6049218a578eb980cb4130
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AT yuxiaohan amultisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
AT longsha amultisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
AT shufangzhu amultisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
AT shangbozhou multisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
AT yuxiaohan multisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
AT longsha multisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
AT shufangzhu multisampleparticleswarmoptimizationalgorithmbasedonelectricfieldforce
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