文章基本信息

标题：An Improved PSO Algorithm with Decline Disturbance Index
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作者：Zhao, Fuqing ; Tang, Jianxin ; Wang, Jizhe 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2011
卷号：6
期号：4
页码：691-697
DOI：10.4304/jcp.6.4.691-697
语种：English
出版社：Academy Publisher
摘要：The particle swarm optimization algorithm (PSO) has two typical problems as in other adaptive evolutionary algorithms, which are based on swarm intelligence search. To deal with the problems of the slow convergence rate and the tendency to trap into premature, an improved particle swarm optimization with decline disturbance index (DDPSO) is presented in this paper. The index was added when the velocity of the particle is prone to stagnation in the middle and later evolution periods. The modification improves the ability of particles to explore the global and local optimization solutions, and reduces the probability of being trapped into the local optima. Theoretical analysis, which is based on stochastic processes, proves that the trajectory of particle is a Markov processes and DDPSO algorithm converges to the global optimal solution with mean square merit. Experimental simulations show that the improved algorithm can not only improve the convergence of the algorithm significantly, but also avoid trapping into local optimization solution.
关键词：particle swarm optimization; premature; stochastic processes; decline disturbance index; convergence