An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line
In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/8204867 |
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| _version_ | 1850226213276090368 |
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| author | Zhengwu Fan Tie Wang Zhi Cheng Guoxing Li Fengshou Gu |
| author_facet | Zhengwu Fan Tie Wang Zhi Cheng Guoxing Li Fengshou Gu |
| author_sort | Zhengwu Fan |
| collection | DOAJ |
| description | In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO). Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature. |
| format | Article |
| id | doaj-art-5e793c8ede404bb9af8cf7eac9a5c464 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5e793c8ede404bb9af8cf7eac9a5c4642025-08-20T02:05:08ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/82048678204867An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to LineZhengwu Fan0Tie Wang1Zhi Cheng2Guoxing Li3Fengshou Gu4Department of Vehicle Engineering, Taiyuan University of Technology, Shanxi, ChinaDepartment of Vehicle Engineering, Taiyuan University of Technology, Shanxi, ChinaDepartment of Mechanical Engineering, Taiyuan University of Science Technology, Shanxi, ChinaDepartment of Vehicle Engineering, Taiyuan University of Technology, Shanxi, ChinaDepartment of Vehicle Engineering, Taiyuan University of Technology, Shanxi, ChinaIn a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO). Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature.http://dx.doi.org/10.1155/2017/8204867 |
| spellingShingle | Zhengwu Fan Tie Wang Zhi Cheng Guoxing Li Fengshou Gu An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line Shock and Vibration |
| title | An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line |
| title_full | An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line |
| title_fullStr | An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line |
| title_full_unstemmed | An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line |
| title_short | An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line |
| title_sort | improved multiobjective particle swarm optimization algorithm using minimum distance of point to line |
| url | http://dx.doi.org/10.1155/2017/8204867 |
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