Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm

Targeting at the slow convergence and the local optimum problems of particle swarm optimization (PSO), a large-scale bi-level particle swarm optimization algorithm is proposed in this paper, which enlarges the particle swarm scale and enhances the initial population diversity on the basis of multi-p...

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Autores principales:	Jia-Jia Jiang, Wen-Xue Wei, Wan-Lu Shao, Yu-Feng Liang, Yuan-Yuan Qu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Bi-level particle swarm swarm intelligence particle swarm optimization large-scale particle swarm Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/18864ff7126740f2913f73e92cfc5dde
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id	oai:doaj.org-article:18864ff7126740f2913f73e92cfc5dde
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spelling	oai:doaj.org-article:18864ff7126740f2913f73e92cfc5dde2021-11-19T00:05:09ZResearch on Large-Scale Bi-Level Particle Swarm Optimization Algorithm2169-353610.1109/ACCESS.2021.3072199https://doaj.org/article/18864ff7126740f2913f73e92cfc5dde2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9399443/https://doaj.org/toc/2169-3536Targeting at the slow convergence and the local optimum problems of particle swarm optimization (PSO), a large-scale bi-level particle swarm optimization algorithm is proposed in this paper, which enlarges the particle swarm scale and enhances the initial population diversity on the basis of multi-particle swarms. On the other hand, this algorithm also improves the running efficiency of the particle swarms by the structural advantages of bi-level particle swarms, for which, the upper-level particle swarm provides decision-making information while the lower level working particle swarms run at the same time, enhancing the operation efficiency of particle swarms. The two levels of particle swarms collaborate and work well with each other. In order to prevent population precocity and slow convergence in the later stage, an accelerated factor based on increasing exponential function is applied at the same time to control the coupling among particle swarms. And the simulation results show that the large-scale bi-level particle swarm optimization algorithm is featured in better superiority and stability.Jia-Jia JiangWen-Xue WeiWan-Lu ShaoYu-Feng LiangYuan-Yuan QuIEEEarticleBi-level particle swarmswarm intelligenceparticle swarm optimizationlarge-scale particle swarmElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 56364-56375 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bi-level particle swarm swarm intelligence particle swarm optimization large-scale particle swarm Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Bi-level particle swarm swarm intelligence particle swarm optimization large-scale particle swarm Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jia-Jia Jiang Wen-Xue Wei Wan-Lu Shao Yu-Feng Liang Yuan-Yuan Qu Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
description	Targeting at the slow convergence and the local optimum problems of particle swarm optimization (PSO), a large-scale bi-level particle swarm optimization algorithm is proposed in this paper, which enlarges the particle swarm scale and enhances the initial population diversity on the basis of multi-particle swarms. On the other hand, this algorithm also improves the running efficiency of the particle swarms by the structural advantages of bi-level particle swarms, for which, the upper-level particle swarm provides decision-making information while the lower level working particle swarms run at the same time, enhancing the operation efficiency of particle swarms. The two levels of particle swarms collaborate and work well with each other. In order to prevent population precocity and slow convergence in the later stage, an accelerated factor based on increasing exponential function is applied at the same time to control the coupling among particle swarms. And the simulation results show that the large-scale bi-level particle swarm optimization algorithm is featured in better superiority and stability.
format	article
author	Jia-Jia Jiang Wen-Xue Wei Wan-Lu Shao Yu-Feng Liang Yuan-Yuan Qu
author_facet	Jia-Jia Jiang Wen-Xue Wei Wan-Lu Shao Yu-Feng Liang Yuan-Yuan Qu
author_sort	Jia-Jia Jiang
title	Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
title_short	Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
title_full	Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
title_fullStr	Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
title_full_unstemmed	Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm
title_sort	research on large-scale bi-level particle swarm optimization algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/18864ff7126740f2913f73e92cfc5dde
work_keys_str_mv	AT jiajiajiang researchonlargescalebilevelparticleswarmoptimizationalgorithm AT wenxuewei researchonlargescalebilevelparticleswarmoptimizationalgorithm AT wanlushao researchonlargescalebilevelparticleswarmoptimizationalgorithm AT yufengliang researchonlargescalebilevelparticleswarmoptimizationalgorithm AT yuanyuanqu researchonlargescalebilevelparticleswarmoptimizationalgorithm
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Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm

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