Research on a Hybrid Optimization Algorithm for Nonlinear Function

摘要：

Particle swarm optimization (PSO) was modified by escape of the particle velocity, and a self-adaptive PSO (SAPSO) was proposed to overcome the PSo shortcomings of the premature convergence and the local optimization. The SAPSO is combined with radial basis function (RBF) neural network to form a SAPSOn hybrid algorithm. Compared with the hybrid algorithM of BP neural network (PSOBP). SAPSON has less adjustable parameters, faster convergence speed, global optimization and higher identification precision in the numerical experiment.

关键词： self-adaptive PSO global optimization nonlinear function radial basis function

作者: Jian Guo Jing Gong Jingbang Xu

作者单位: Wuhan Polytechnic University, Wuhan 430023, P.R. China Department of Controlled Science & Engineering, Huazhong University of Science & Technology, Wuhan 4

会议类型: 国际会议

会议名称: The First World Congress on Global Optimization in Engineering & Science(第一届工程与科学全局优化国际会议 WCGO2009)

会议地点: 长沙

会议语种:英文

页码: 851-856

在线出版日期: 2009-06-01（万方平台首次上网日期，不代表论文的发表时间）