Adaptive Quantum-behaved Particle Swarm Algorithm with Dynamically Changing Inertia Weight

摘要：

A new quantum-behaved particle swarm algorithm with dynamically changing inertia weight is presented to solve the problem that the linearly decreasing weight of the quantum-behaved particle swarm algorithm cannot adapt to the complex and nonlinear optimization process. The evolution speed factor and aggregation degree factor of the swarm a reintroduced in this new algorithm and the weight is formulated as a function of these two factors according to their impact on the search performance of the swarm. In each iteration process,the weight is changed dynamically based on the evolution speed factor and aggregation degree factor,which provides the algorithm with effective dynamic adaptability.The algorithms of quantum-behaved particle swarm are tested with Sphere benchmark functions.The experiments show that the convergence speed of quantum-behaved particle swarm algorithm is significantly superior to adaptive quantum-behaved particle swarm algorithm.

关键词： quantum-behaved particle swarm adaptability inertia weight

作者: ZeXia Huang YouHong Yu

作者单位: College of information Engineering ,Zhe Jiang University of Technology, Hangzhou, Zhejiang, China De College of science, Zhe Jiang University of Technology, Hangzhou, Zhejiang, China

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: 78-81

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）