Self-adaptive Particle Swarm Optimization Algorithm with Mutation Operation based on K-means

摘要：

　　Adaptive Particle Swarm Optimization algorithm with mutation operation based on Kmeans is proposed in this paper,this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization,the algorithm self-adaptively adjusted inertia weight according to fitness variance of population.Mutation operation was peocessed for the poor performative particle in population.The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means,and more effectively improved clustering quality.

关键词： k-means cluster algorithm Particle Swarm Optimization mutation

作者: Xue-mei Wang Yi-zhuo Guo Gui-jun Liu

作者单位: Department of Computer Science and Technology,Cheng-dong College of Northeast Agricultural University Harbin,150025,China

会议类型: 国际会议

会议名称: 2013 2nd international Conference on Opto-Electronics Engineering and Materials Eesearch(2013第二届光电工程与材料研究国际会议)(OEMR2013)

会议地点: 郑州

会议语种:英文

页码: 1260-1264

在线出版日期: 2013-10-19（万方平台首次上网日期，不代表论文的发表时间）