Application of Particle Swarm Optimization Algorithm Based on Cloud Model for Path Planning

摘要：

The penalty function was used to change the constrained problem into the unconstrained problem in path planning of robots in this paper. Utilizing the random icity and stable tendentiousness characteristics of cloud model, an adaptive adjusting parameters strategy for particle pwarm optimization (PSO) theory is introduced. So an improved PSO algorithm was constructed and applied to path planning of robots. By adopting different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation was balacnced effectively. The scheme does not only improve the convergence speed, but also maintain the diversity of the population as well. The feasibility and effectiveness were validated by the simulation experiments.

关键词： cloud model particle swarm optimization (PSO) algorithms path planning adaptive adjusting parameters

作者: Liansuo Wei Xuefeng Dai

作者单位: Computer & Control Engineering College, Qiqihar University, Qiqihar, Heilongjiang, 161006

会议类型: 国际会议

会议名称: 2010 International Conference on Circuit and Signal Processing(2010年电路与信号处理国际会议 ICCSP 2010)

会议地点: 上海

会议语种:英文

页码: 471-474

在线出版日期: 2010-12-25（万方平台首次上网日期，不代表论文的发表时间）