Particle Swarm Optimization with group decision making

摘要：

The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses own experience and other’s to make decision, it is easy to trap into premature convergence, but group decision making with all the individuals to make decisions uses various experiences and viewpoints to get better plan for avoiding conformity. A new formal particle swarm optimization is advanced basing on group decision(GDPSO) it takes each particle as an individual decision-maker and uses the basic information of particle such as the position of individual history and fitness value to decide a new position, then using the position replaces the global best position(pgj),So the space of searching is expanded and the population diversity is increased through the new improved algorithm, it can improve the convergence speed and the capacity of global searching, the premature convergence is avoided to some degree.

关键词： particle swarm optimization premature convergence group decision making

作者: Liang Wang Zhihua Cui Jianchao Zeng

作者单位: Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan, Shanxi, PR.China, 030024

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）