A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy
When faced with complex optimization problems with multiple objectives and multiple variables, many multiobjective particle swarm algorithms are prone to premature convergence. To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm op...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/1626457 |
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| author | Kangge Zou Yanmin Liu Shihua Wang Nana Li Yaowei Wu |
| author_facet | Kangge Zou Yanmin Liu Shihua Wang Nana Li Yaowei Wu |
| author_sort | Kangge Zou |
| collection | DOAJ |
| description | When faced with complex optimization problems with multiple objectives and multiple variables, many multiobjective particle swarm algorithms are prone to premature convergence. To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm optimization algorithm based on the grid technique and multistrategy (GTMSMOPSO) is proposed. The algorithm randomly uses one of two different evaluation index strategies (convergence evaluation index and distribution evaluation index) combined with the grid technique to enhance the diversity and convergence of the population and improve the probability of particles flying to the real Pareto front. A combination of grid technology and a mixed evaluation index strategy is used to maintain the external archive to avoid removing particles with better convergence based only on particle density, which leads to population degradation and affects the particle exploitation ability. At the same time, a variation operation is proposed to avoid rapid degradation of the population, which enhances the particle search capability. The simulation results show that the proposed algorithm has better convergence and distribution than CMOPSO, NSGAII, MOEAD, MOPSOCD, and NMPSO. |
| format | Article |
| id | doaj-art-840707958b474a21b91b604a01d219c0 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-840707958b474a21b91b604a01d219c02025-02-03T01:26:56ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/1626457A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and MultistrategyKangge Zou0Yanmin Liu1Shihua Wang2Nana Li3Yaowei Wu4School of Mathematics and StatisticsZunyi Normal UniversitySchool of Mathematics and StatisticsSchool of Data Science and Information EngineeringSchool of Mathematics and Computational StatisticsWhen faced with complex optimization problems with multiple objectives and multiple variables, many multiobjective particle swarm algorithms are prone to premature convergence. To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm optimization algorithm based on the grid technique and multistrategy (GTMSMOPSO) is proposed. The algorithm randomly uses one of two different evaluation index strategies (convergence evaluation index and distribution evaluation index) combined with the grid technique to enhance the diversity and convergence of the population and improve the probability of particles flying to the real Pareto front. A combination of grid technology and a mixed evaluation index strategy is used to maintain the external archive to avoid removing particles with better convergence based only on particle density, which leads to population degradation and affects the particle exploitation ability. At the same time, a variation operation is proposed to avoid rapid degradation of the population, which enhances the particle search capability. The simulation results show that the proposed algorithm has better convergence and distribution than CMOPSO, NSGAII, MOEAD, MOPSOCD, and NMPSO.http://dx.doi.org/10.1155/2021/1626457 |
| spellingShingle | Kangge Zou Yanmin Liu Shihua Wang Nana Li Yaowei Wu A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy Journal of Mathematics |
| title | A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy |
| title_full | A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy |
| title_fullStr | A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy |
| title_full_unstemmed | A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy |
| title_short | A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy |
| title_sort | multiobjective particle swarm optimization algorithm based on grid technique and multistrategy |
| url | http://dx.doi.org/10.1155/2021/1626457 |
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