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

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 individuals DCD was based on the degree of difference between the crowding distances on different objectives. The proposed approach computed individuals DCD dynamically during the process of population maintenance to ensure sufticient 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 DCDMOPSO.

关键词： particle swarm algorithm multi-objective optimization dynamic crowding distance Pareto set

作者: Liqin Liu Xueliang Zhang Liming Xie Juan Du

作者单位: College of Mechanical Electronic Engineering,Lanzhou University of Technology,85 Langongping,Lanzhou College of Mechanical Electronic Engineering,Lanzhou University of Technology,85 Langongping,Lanzhou College of Mechanical Electronic Engineering,Taiyuan University of Science and Technology,66 Waliu R

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 481-485

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）