An Adaptive Particle Swarm Optimization Algorithm and Simulation
To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.
particle swarm optimization inertia weight population diversity
Zhang Dingxue Guan Zhihong Liu Xinzhi
Department of control science and engineering Huazhong University of Science & Technology Wuhan, Hubei Province, china
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）