An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line

Zhengwu Fan, Tie Wang, Zhi Cheng, Guoxing Li, Fengshou Gu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number8204867
Number of pages16
JournalShock and Vibration
Volume2017
DOIs
Publication statusPublished - 25 Sep 2017

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Multiobjective optimization
Particle swarm optimization (PSO)
Tires
optimization
tires
tire
Seats
particle
seats
vibration
vehicles
degrees of freedom

Cite this

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title = "An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line",
abstract = "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.",
author = "Zhengwu Fan and Tie Wang and Zhi Cheng and Guoxing Li and Fengshou Gu",
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An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line. / Fan, Zhengwu; Wang, Tie; Cheng, Zhi; Li, Guoxing; Gu, Fengshou.

In: Shock and Vibration, Vol. 2017, 8204867, 25.09.2017.

Research output: Contribution to journalArticle

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