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	A Novel Multi-Objective Particle Swarm Optimization based on Dynamic Crowding Distance
	Liu, Liqin1,2; Zhang, Xueliang1,2; Xie, Liming2; Du, Juan1
	2009
关键词	particle swarm algorithm multi-objective optimization dynamic crowding distance Pareto set
页码	481-+
英文摘要	In this article, a multi-objective particle swarm optimization algorithm based on dynamic crowding distance (DCD-MOPSO) was proposed, in which the definition of DCD was based on the degree of difference between the crowding distances on different objectives The proposed approach computed individual's DCD dynamically during the process of population maintenance to ensure sufficient diversity amongst the solutions of the non-dominated fronts Introducing the improved quick sorting to reduce the time for computation, both the dynamic inertia weight and acceleration coefficients are used in the algorithm to explore the search space more efficiently Experiments on well known and widely used test problems are performed, aiming at investigating the convergence and solution diversity of DCD-MOPSO The obtained results are compared with MOPSO and NSGA-II, yielding the superiority of DCD-MOPSO
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000284970100101
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/37870]
专题	兰州理工大学 机电工程学院
作者单位	1.Taiyuan Univ Sci & Technol, Coll Mech Elect Engn, Taiyuan 030024, Shanxi, Peoples R China 2.Lanzhou Univ Technol, Coll Mech Elect Engn, 85 Langongping, Lanzhou 730050, Gansu, Peoples R China;
推荐引用方式 GB/T 7714	Liu, Liqin,Zhang, Xueliang,Xie, Liming,et al. A Novel Multi-Objective Particle Swarm Optimization based on Dynamic Crowding Distance[C]. 见:.

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