A Novel Multi-Objective Particle Swarm Optimization based on Dynamic Crowding Distance

被引：1

作者：

Liu, Liqin ^{[1
,2
]}

Zhang, Xueliang ^{[1
,2
]}

Xie, Liming ^{[1
]}

Du, Juan ^{[2
]}

机构：

[1] Lanzhou Univ Technol, Coll Mech Elect Engn, 85 Langongping, Lanzhou 730050, Gansu, Peoples R China

[2] Taiyuan Univ Sci & Technol, Coll Mech Elect Engn, Taiyuan 030024, Shanxi, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年

基金：

中国国家自然科学基金;

关键词：

particle swarm algorithm; multi-objective optimization; dynamic crowding distance; Pareto set;

D O I：

10.1109/ICICISYS.2009.5357798

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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

引用

页码：481 / +

页数：2

共 16 条

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