Adaptive Chaos Particle Swarm Optimization Algorithm

摘要：

An adaptive chaos particle swarm optimization (ACPSO) algorithm is presented based on several improvements in original PSO. First,ACPSO improves the performances of the standard PSO by applying chaos searching mechanism to avoid premature convergence. Second,the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm. Third,a variable is introduced to describe quantitatively the convergence status of the particle swarm,which can be used as the evaluating condition of the convergence criterion. The experimental results show that the proposed algorithm not only has great advantages of convergence property over standard PSO and some other modified PSO algorithms,but also avoids effectively being trapped in local minima.

关键词： PSO chaos adaptive inertia weight premature convergence global optimization

作者: Zhao Zhigang Chang Cheng Zhang Fugang

作者单位: College of Computer and Electronics Information,Guangxi University,Nanning 530004,China College of Computer and Electronics Information,Guangxi niversity,Nanning 530004,China

会议类型: 国际会议

会议名称: 2010 International Forum on Computer Science-Technology and Applications(2010 国际计算机科学技术应用论坛 IFCSTA 2010)

会议地点: 南宁

会议语种:英文

页码: 167-170

在线出版日期: 2010-12-10（万方平台首次上网日期，不代表论文的发表时间）