Multiobjective Particle Swarm Optimization Based on Ideal Distance
Recently, multiobjective particle swarm optimization (MOPSO) has been widely used in science and engineering, however how to effectively improve the convergence and distribution of the algorithm has always been a hot research topic on multiobjective optimization problems (MOPs). To solve this proble...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/3515566 |
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| _version_ | 1832565299885375488 |
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| author | Shihua Wang Yanmin Liu Kangge Zou Nana Li Yaowei Wu |
| author_facet | Shihua Wang Yanmin Liu Kangge Zou Nana Li Yaowei Wu |
| author_sort | Shihua Wang |
| collection | DOAJ |
| description | Recently, multiobjective particle swarm optimization (MOPSO) has been widely used in science and engineering, however how to effectively improve the convergence and distribution of the algorithm has always been a hot research topic on multiobjective optimization problems (MOPs). To solve this problem, we propose a multiobjective particle swarm optimization based on the ideal distance (IDMOPSO). In IDMOPSO, the adaptive grid and ideal distance are used to optimize and improve the selection method of global learning samples and the size control strategy of the external archive, and the fine-tuning parameters are introduced to adjust particle flight in the swarm dynamically. Additionally, to prevent the algorithm from falling into a local optimum, the cosine factor is introduced to mutate the position of the particles during the exploitation and exploration process. Finally, IDMOPSO, several other popular MOPSOs and MOEAs were simulated on the benchmarks functions to test the performance of the proposed algorithm using IGD and HV indicators. The experimental results show that IDMOPSO has the better convergence, diversity, and excellent solution ability compared to the other algorithms. |
| format | Article |
| id | doaj-art-4c790b9149ac4f2bbbd8ee53ac492304 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-4c790b9149ac4f2bbbd8ee53ac4923042025-02-03T01:08:01ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/3515566Multiobjective Particle Swarm Optimization Based on Ideal DistanceShihua Wang0Yanmin Liu1Kangge Zou2Nana Li3Yaowei Wu4School of Mathematics and StatisticsZunyi Normal UniversitySchool of Mathematics and StatisticsSchool of Data Science and Information EngineeringSchool of Mathematics and Computational StatisticsRecently, multiobjective particle swarm optimization (MOPSO) has been widely used in science and engineering, however how to effectively improve the convergence and distribution of the algorithm has always been a hot research topic on multiobjective optimization problems (MOPs). To solve this problem, we propose a multiobjective particle swarm optimization based on the ideal distance (IDMOPSO). In IDMOPSO, the adaptive grid and ideal distance are used to optimize and improve the selection method of global learning samples and the size control strategy of the external archive, and the fine-tuning parameters are introduced to adjust particle flight in the swarm dynamically. Additionally, to prevent the algorithm from falling into a local optimum, the cosine factor is introduced to mutate the position of the particles during the exploitation and exploration process. Finally, IDMOPSO, several other popular MOPSOs and MOEAs were simulated on the benchmarks functions to test the performance of the proposed algorithm using IGD and HV indicators. The experimental results show that IDMOPSO has the better convergence, diversity, and excellent solution ability compared to the other algorithms.http://dx.doi.org/10.1155/2022/3515566 |
| spellingShingle | Shihua Wang Yanmin Liu Kangge Zou Nana Li Yaowei Wu Multiobjective Particle Swarm Optimization Based on Ideal Distance Discrete Dynamics in Nature and Society |
| title | Multiobjective Particle Swarm Optimization Based on Ideal Distance |
| title_full | Multiobjective Particle Swarm Optimization Based on Ideal Distance |
| title_fullStr | Multiobjective Particle Swarm Optimization Based on Ideal Distance |
| title_full_unstemmed | Multiobjective Particle Swarm Optimization Based on Ideal Distance |
| title_short | Multiobjective Particle Swarm Optimization Based on Ideal Distance |
| title_sort | multiobjective particle swarm optimization based on ideal distance |
| url | http://dx.doi.org/10.1155/2022/3515566 |
| work_keys_str_mv | AT shihuawang multiobjectiveparticleswarmoptimizationbasedonidealdistance AT yanminliu multiobjectiveparticleswarmoptimizationbasedonidealdistance AT kanggezou multiobjectiveparticleswarmoptimizationbasedonidealdistance AT nanali multiobjectiveparticleswarmoptimizationbasedonidealdistance AT yaoweiwu multiobjectiveparticleswarmoptimizationbasedonidealdistance |