Multi-objective particle swarm optimization with reverse multi-leaders
Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-05-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023522?viewType=HTML |
| _version_ | 1797814497702838272 |
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| author | Fei Chen Yanmin Liu Jie Yang Meilan Yang Qian Zhang Jun Liu |
| author_facet | Fei Chen Yanmin Liu Jie Yang Meilan Yang Qian Zhang Jun Liu |
| author_sort | Fei Chen |
| collection | DOAJ |
| description | Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversity of solutions in the archives. Second, a reverse selection method is proposed to select two global leaders for the particles in the population. This is conducive to selecting appropriate learning samples for each particle and leading the particles to quickly fly to the true Pareto front. Third, an information fusion strategy is proposed to update the personal best, to improve convergence of the algorithm. At the same time, in order to achieve a better balance between convergence and diversity, a new particle velocity updating method is proposed. With this, two global leaders cooperate to guide the flight of particles in the population, which is conducive to promoting the exchange of social information. Finally, RMMOPSO is simulated with several state-of-the-art MOPSOs and multi-objective evolutionary algorithms (MOEAs) on 22 benchmark problems. The experimental results show that RMMOPSO has better comprehensive performance. |
| first_indexed | 2024-03-13T08:08:37Z |
| format | Article |
| id | doaj.art-ab7ad7b50d564b57bf62849352e36a5e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-13T08:08:37Z |
| publishDate | 2023-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-ab7ad7b50d564b57bf62849352e36a5e2023-06-01T01:19:42ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-05-01207117321176210.3934/mbe.2023522Multi-objective particle swarm optimization with reverse multi-leadersFei Chen0Yanmin Liu1Jie Yang2Meilan Yang3Qian Zhang 4Jun Liu51. School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China2. School of Mathematics, Zunyi Normal College, Zunyi 563002, China2. School of Mathematics, Zunyi Normal College, Zunyi 563002, China1. School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China1. School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China3. School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, ChinaDespite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversity of solutions in the archives. Second, a reverse selection method is proposed to select two global leaders for the particles in the population. This is conducive to selecting appropriate learning samples for each particle and leading the particles to quickly fly to the true Pareto front. Third, an information fusion strategy is proposed to update the personal best, to improve convergence of the algorithm. At the same time, in order to achieve a better balance between convergence and diversity, a new particle velocity updating method is proposed. With this, two global leaders cooperate to guide the flight of particles in the population, which is conducive to promoting the exchange of social information. Finally, RMMOPSO is simulated with several state-of-the-art MOPSOs and multi-objective evolutionary algorithms (MOEAs) on 22 benchmark problems. The experimental results show that RMMOPSO has better comprehensive performance.https://www.aimspress.com/article/doi/10.3934/mbe.2023522?viewType=HTMLmulti-objective particle swarm optimizationglobal rankingmean angular distancereverse selectioninformation fusion |
| spellingShingle | Fei Chen Yanmin Liu Jie Yang Meilan Yang Qian Zhang Jun Liu Multi-objective particle swarm optimization with reverse multi-leaders Mathematical Biosciences and Engineering multi-objective particle swarm optimization global ranking mean angular distance reverse selection information fusion |
| title | Multi-objective particle swarm optimization with reverse multi-leaders |
| title_full | Multi-objective particle swarm optimization with reverse multi-leaders |
| title_fullStr | Multi-objective particle swarm optimization with reverse multi-leaders |
| title_full_unstemmed | Multi-objective particle swarm optimization with reverse multi-leaders |
| title_short | Multi-objective particle swarm optimization with reverse multi-leaders |
| title_sort | multi objective particle swarm optimization with reverse multi leaders |
| topic | multi-objective particle swarm optimization global ranking mean angular distance reverse selection information fusion |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023522?viewType=HTML |
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