Improved Particle Swarm Optimization Algorithm Based on Random Perturbations

摘要：

This paper proposed an novel improved particle swarm optimizer algorithm based on random perturbations (PSO-RP) with global convergence performance. Random perturbations are introduced to improve the performance of global convergence of the particle swarm optimizer (PSO). The novel search strategy enables the PSO-RP to make use of random information, in addition to experience, to achieve better quality solutions. Simulations show the novel random search strategy enables the PSO-RP to own the performance of global convergence. Five of well-known benchmarks used in evolutionary optimization methods are used to evaluate the performance of the PSO-RP. From experiments, we observe that the PSO-RP significantly improves the PSOs performance and performs better than the basic PSO and other recent variants of PSO.

作者: Xiao Xiao Congli Mei Guohai Liu

作者单位: Department of Automation, Jiangsu University, Zhenjiang, China

会议类型: 国际会议

会议名称: The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

会议地点: 黄山

会议语种:英文

页码: 404-408

在线出版日期: 2010-05-28（万方平台首次上网日期，不代表论文的发表时间）