Common model analysis and improvement of particle swarm optimizer
Feng PAN
Jie CHEN
Minggang GAN
Guanghui WANG
Tao CAI
摘要：Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum.However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm.In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to (-1, 1). Based on the deduced principle of acceleration factors, a new adaptive PSO algorithmharmonious PSO (HPSO) is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments,HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.
机标关键词：particle swarm optimizerevolutionary computationsufficient conditionsasymptotic stabilityidentification ofsearch algorithmfitness functionglobal minimum
分类号：TP3(计算技术、计算机技术)
资助基金：the Teaching and Research Award Program for Outstanding Young Teacher in Higher Education Institute of Ministry of Education of China(20010248)
论文发表日期：2007-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 233-238 )
英文信息