A Diversity Guided Particles Swarm Optimization

摘要：

　　A new particle swarm optimization algorithm (a diversity guided particles swarm Optimization),which is guided by population diversity,is proposed.In order to overcome the premature convergence of the algorithm,a metric to measure the swarm diversity is designed,the update of velocity and position of particles is controlled by this criteria,and the four sub-processes are introduced in the process of updating in order to increase the swarm diversity,which enhance to the ability of particle swarm optimization algorithm (PSO) to break away from the local optimum.The experimental results exhibit that the new algorithm not only has great advantage of global search capability,but also can avoid the premature convergence problem effectively.

关键词： particles swarm optimization population diversity premature convergence

作者: Na Li Yuanxiang Li

作者单位: State Key Lab.of Software Engineering, Wuhan University, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1429-1433

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）