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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doaj-18864ff7126740f2913f73e92cfc5dde2021-04-16T23:00:27ZengIEEEIEEE Access2169-35362021-01-019563645637510.1109/ACCESS.2021.30721999399443Research on Large-Scale Bi-Level Particle Swarm Optimization AlgorithmJia-Jia Jiang0https://orcid.org/0000-0002-6437-6360Wen-Xue Wei1https://orcid.org/0000-0002-8199-163XWan-Lu Shao2https://orcid.org/0000-0002-2027-1807Yu-Feng Liang3https://orcid.org/0000-0002-8199-163XYuan-Yuan Qu4https://orcid.org/0000-0002-3350-186XCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaTargeting 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.https://ieeexplore.ieee.org/document/9399443/Bi-level particle swarmswarm intelligenceparticle swarm optimizationlarge-scale particle swarm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jia-Jia Jiang Wen-Xue Wei Wan-Lu Shao Yu-Feng Liang Yuan-Yuan Qu |
spellingShingle |
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 IEEE Access Bi-level particle swarm swarm intelligence particle swarm optimization large-scale particle swarm |
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 |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
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. |
topic |
Bi-level particle swarm swarm intelligence particle swarm optimization large-scale particle swarm |
url |
https://ieeexplore.ieee.org/document/9399443/ |
work_keys_str_mv |
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