A MODIFIED PARTICLE SWARM OPTIMIZATION FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

摘要：

This paper proposes a Modified Particle Swarm Optimization based on the combining Attractive and Repulsive operator with Function Stretching technique (for short MPSOwARS). This new algorithm utilizes adequately the characters that the Attractive and Repulsive operator can efficiently ensure diversity of swarm and make algorithm prevent premature convergence, and the characters that Function Stretching technique can decrease efficiently the complexity of objective function. The results of the experiment on benchmark function are presented. Conclusions show that the MPSOwARS algorithm performs better than previous works and adapts to solve very complex multidimensional and multi-modal global optimization problems especially.

关键词： Global Optimization PSO Attractive and Repulsive Stretching Technique

作者: YI-CHAO HE KUN-QI LIU

作者单位: Information Project Department,Shijiazhuang University of Economics Hebei Shijazhuang 050031,China Information Project Department,Shijiazhuang University of Economics Hebei Shijazhuang 050031,China;C

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2173-2177

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）