A Novel Particle Swarm Optimization Method Using Clonai Selection Algorithm

摘要：

Particle Swarm Optimization, a nature-inspired evolutionary algorithm, has been successful in solving a wide range of real-value optimization problems. However, little attempts have been made to extend it to discrete problems. In this paper, a new particle swarm optimization method based on the clonal selection algorithm is proposed to avoid premature convergence and guarantee the diversity of the population. The experimental results show that the new algorithm not only has great advantage of convergence property over clonal selection algorithm and PSO, but also can avoid the premature convergence problem effectively.

关键词： particle swarm optimization clonal selection algorithm premature diversity

作者: Lu Hong

作者单位: Department of Electronic Engineering Huaihai Institute of Technology Lianyungang,China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 1415-1418

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）