Reinforcement-learning-based parameter adaptation method for particle swarm optimization
Abstract Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence. In this article, a reinforcement-learning-based parameter adaptation method (RLAM) is dev...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-03-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01012-8 |
| _version_ | 1797674947145891840 |
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| author | Shiyuan Yin Min Jin Huaxiang Lu Guoliang Gong Wenyu Mao Gang Chen Wenchang Li |
| author_facet | Shiyuan Yin Min Jin Huaxiang Lu Guoliang Gong Wenyu Mao Gang Chen Wenchang Li |
| author_sort | Shiyuan Yin |
| collection | DOAJ |
| description | Abstract Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence. In this article, a reinforcement-learning-based parameter adaptation method (RLAM) is developed to enhance the PSO convergence by designing a network to control the coefficients of the PSO. Moreover, based on the RLAM, a new reinforcement-learning-based PSO (RLPSO) algorithm is designed. To investigate the performance of the RLAM and RLPSO, experiments on 28 CEC 2013 benchmark functions were carried out to compare with other adaptation methods and PSO variants. The reported computational results showed that the proposed RLAM is efficient and effective and that the proposed RLPSO is superior to several state-of-the-art PSO variants. |
| first_indexed | 2024-03-11T22:07:34Z |
| format | Article |
| id | doaj.art-0ec9c37dbdb545d4b8f6ba632d9674fc |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-11T22:07:34Z |
| publishDate | 2023-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-0ec9c37dbdb545d4b8f6ba632d9674fc2023-09-24T11:35:45ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-03-01955585560910.1007/s40747-023-01012-8Reinforcement-learning-based parameter adaptation method for particle swarm optimizationShiyuan Yin0Min Jin1Huaxiang Lu2Guoliang Gong3Wenyu Mao4Gang Chen5Wenchang Li6High Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASHigh Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASHigh Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASHigh Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASHigh Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASHigh Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASHigh Speed Circuit and Neural Network Laboratory, Institute of Semiconductors, CASAbstract Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence. In this article, a reinforcement-learning-based parameter adaptation method (RLAM) is developed to enhance the PSO convergence by designing a network to control the coefficients of the PSO. Moreover, based on the RLAM, a new reinforcement-learning-based PSO (RLPSO) algorithm is designed. To investigate the performance of the RLAM and RLPSO, experiments on 28 CEC 2013 benchmark functions were carried out to compare with other adaptation methods and PSO variants. The reported computational results showed that the proposed RLAM is efficient and effective and that the proposed RLPSO is superior to several state-of-the-art PSO variants.https://doi.org/10.1007/s40747-023-01012-8Particle swarm optimizationReinforcement learningCEC 2013 benchmark |
| spellingShingle | Shiyuan Yin Min Jin Huaxiang Lu Guoliang Gong Wenyu Mao Gang Chen Wenchang Li Reinforcement-learning-based parameter adaptation method for particle swarm optimization Complex & Intelligent Systems Particle swarm optimization Reinforcement learning CEC 2013 benchmark |
| title | Reinforcement-learning-based parameter adaptation method for particle swarm optimization |
| title_full | Reinforcement-learning-based parameter adaptation method for particle swarm optimization |
| title_fullStr | Reinforcement-learning-based parameter adaptation method for particle swarm optimization |
| title_full_unstemmed | Reinforcement-learning-based parameter adaptation method for particle swarm optimization |
| title_short | Reinforcement-learning-based parameter adaptation method for particle swarm optimization |
| title_sort | reinforcement learning based parameter adaptation method for particle swarm optimization |
| topic | Particle swarm optimization Reinforcement learning CEC 2013 benchmark |
| url | https://doi.org/10.1007/s40747-023-01012-8 |
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