A Novel Adaptive PSO Algorithm on Schaffer’s F6 Function

摘要：

Analyzing the distance between the location and the new location, we conclude inertia weight method which linearly decreases from 0.9 to 0.4 has the powerful local search ability on Schaffer’s F6 function. In order to improve the balance between local and global search ability, the novel adaptive PSO algorithm which evaluates a reset function to control the inertia weight value is proposed. Once plunged into the local optimum, inertia weight, pbest and gbest should be reset to get away from the local optimum. Compared with the particle’s traces, the novel algorithm has a great potential advantage. Simulation results show that the novel adaptive algorithm is better than the inertia weight algorithm in terms of the successful searching rate on Schaffer’s F6 function.

关键词： Particle Swarm Optimization Optimization Algorithm

作者: Xiaohong Qiu Jun Liu

作者单位: School of Software Jiangxi Agricultural University Nanchang 330045, P. R. China College of Computer Jiangxi Normal University Nanchang 330022, P. R. China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-5

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）