A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization
The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method’s accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual. However, excessive aggregation can narrow the sea...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10056156/ |
| _version_ | 1827973775835004928 |
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| author | Jia Guo Guoyuan Zhou Yi Di Binghua Shi Ke Yan Yuji Sato |
| author_facet | Jia Guo Guoyuan Zhou Yi Di Binghua Shi Ke Yan Yuji Sato |
| author_sort | Jia Guo |
| collection | DOAJ |
| description | The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method’s accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual. However, excessive aggregation can narrow the search range of the population, and thus the population may be trapped by local optima. To overcome this problem, a bare-bones particle swarm optimization with crossed memory (BPSO-CM) is proposed in this work. The BPSO-CM contains a multi-memory storage mechanism (MSM) and an elite offspring selection strategy (EOSS). The MSM enables an extra storage space to extend the search ability of the particle swarm and the EOSS enhances the local minimum escaping ability of the particle swarm. The population is endowed with the ability of enhanced global search through the cooperation of the MSM and the EOSS. To verify the performance of the BPSO-CM, the CEC2017 benchmark functions are used in experiments, five population-based methods are selected in the control group. Finally, experimental results proved that the BPSO-CM can present highly accurate results for global optimization problems. |
| first_indexed | 2024-04-09T19:43:09Z |
| format | Article |
| id | doaj.art-595c9aba9bb346e19e081921e9bbb476 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T19:43:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-595c9aba9bb346e19e081921e9bbb4762023-04-03T23:00:35ZengIEEEIEEE Access2169-35362023-01-0111315493156810.1109/ACCESS.2023.325022810056156A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global OptimizationJia Guo0https://orcid.org/0000-0001-5042-4045Guoyuan Zhou1Yi Di2https://orcid.org/0000-0002-2273-6495Binghua Shi3https://orcid.org/0000-0003-4469-5759Ke Yan4Yuji Sato5https://orcid.org/0000-0001-8387-7710School of Information Engineering, Hubei University of Economics, Wuhan, ChinaSchool of Information Engineering, Hubei University of Economics, Wuhan, ChinaSchool of Information Engineering, Hubei University of Economics, Wuhan, ChinaSchool of Information Engineering, Hubei University of Economics, Wuhan, ChinaChina Construction Third Engineering Bureau Installation Engineering Company Ltd., Wuhan, ChinaFaculty of Computer and Information Sciences, Hosei University, Tokyo, JapanThe offspring selection strategy is the core of evolutionary algorithms, which directly affects the method’s accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual. However, excessive aggregation can narrow the search range of the population, and thus the population may be trapped by local optima. To overcome this problem, a bare-bones particle swarm optimization with crossed memory (BPSO-CM) is proposed in this work. The BPSO-CM contains a multi-memory storage mechanism (MSM) and an elite offspring selection strategy (EOSS). The MSM enables an extra storage space to extend the search ability of the particle swarm and the EOSS enhances the local minimum escaping ability of the particle swarm. The population is endowed with the ability of enhanced global search through the cooperation of the MSM and the EOSS. To verify the performance of the BPSO-CM, the CEC2017 benchmark functions are used in experiments, five population-based methods are selected in the control group. Finally, experimental results proved that the BPSO-CM can present highly accurate results for global optimization problems.https://ieeexplore.ieee.org/document/10056156/Crossed memoryparticle swarm optimizationelite offspring selection |
| spellingShingle | Jia Guo Guoyuan Zhou Yi Di Binghua Shi Ke Yan Yuji Sato A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization IEEE Access Crossed memory particle swarm optimization elite offspring selection |
| title | A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization |
| title_full | A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization |
| title_fullStr | A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization |
| title_full_unstemmed | A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization |
| title_short | A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization |
| title_sort | bare bones particle swarm optimization with crossed memory for global optimization |
| topic | Crossed memory particle swarm optimization elite offspring selection |
| url | https://ieeexplore.ieee.org/document/10056156/ |
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