Predicted Particle Swarm Optimization

摘要：

The standard particle swarm optimization (PSO) may prematurely converge on suboptimal solution partly because of the insufficiency information utilization of the velocity. The time cost by velocity is longer than position of each particle of the swarm, though the velocity, limited by the constant Vmax, onfy provides the positional displacement To avoid premature convergence, a new modified PSO, predicted PSO, is proposed owning two different swarms in which the velocity without limitation, considered as a predictor, is used to explore the search space besides providing the displacement while the position considered as a corrector. The algorithm gives some balance between global and local search capability. The optimization computing of some examples is made to show the new algorithm has better global search capacity and rapid convergence rate.

关键词： Particle swarm optimization Predicted velocity Exploration capability Exploitation capability

作者: Zhihua Cui Jianchao Zeng Guoji Sun

作者单位: State Key Laboratory for Manufacturing Systems Engineering, Xian Jiaotong University, Xian. 710049 Division of System Simulation and Computer Application, Taiyuan University of Science and Technology State Key Laboratory for Manufacturing Systems Engineering, Xian Jiaotong University, Xian. 710049

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 658-661

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）