Binary Restructuring Particle Swarm Optimization and Its Application
Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RP...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/2/266 |
| _version_ | 1827738322133319680 |
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| author | Jian Zhu Jianhua Liu Yuxiang Chen Xingsi Xue Shuihua Sun |
| author_facet | Jian Zhu Jianhua Liu Yuxiang Chen Xingsi Xue Shuihua Sun |
| author_sort | Jian Zhu |
| collection | DOAJ |
| description | Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features. |
| first_indexed | 2024-03-11T02:42:44Z |
| format | Article |
| id | doaj.art-68684002d96f4a93ab69a86fb6732a20 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T02:42:44Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-68684002d96f4a93ab69a86fb6732a202023-11-18T09:30:02ZengMDPI AGBiomimetics2313-76732023-06-018226610.3390/biomimetics8020266Binary Restructuring Particle Swarm Optimization and Its ApplicationJian Zhu0Jianhua Liu1Yuxiang Chen2Xingsi Xue3Shuihua Sun4School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, ChinaRestructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.https://www.mdpi.com/2313-7673/8/2/266particle swarm optimizationbinary particle swarm optimizationrestructuring particle swarm optimizationfeature selection |
| spellingShingle | Jian Zhu Jianhua Liu Yuxiang Chen Xingsi Xue Shuihua Sun Binary Restructuring Particle Swarm Optimization and Its Application Biomimetics particle swarm optimization binary particle swarm optimization restructuring particle swarm optimization feature selection |
| title | Binary Restructuring Particle Swarm Optimization and Its Application |
| title_full | Binary Restructuring Particle Swarm Optimization and Its Application |
| title_fullStr | Binary Restructuring Particle Swarm Optimization and Its Application |
| title_full_unstemmed | Binary Restructuring Particle Swarm Optimization and Its Application |
| title_short | Binary Restructuring Particle Swarm Optimization and Its Application |
| title_sort | binary restructuring particle swarm optimization and its application |
| topic | particle swarm optimization binary particle swarm optimization restructuring particle swarm optimization feature selection |
| url | https://www.mdpi.com/2313-7673/8/2/266 |
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