A Quantum-Particle Swarm Evolutionary Algorithm

摘要：

To improve the search ability of particle swarm optimization and prevent premature convergence, a quantum-particle swarm hybrid evolutionary algorithm is proposed by merging quantum evolutionary algorithm and particle swarm optimization. The algorithms evolution process is divided into two phases. In first phase improved QEA is used to search in order to obtain the information about the best solution. And in the second phase, we use PSO to further search to get the final best solution. Applying this algorithm to the optimization of function extremum and weights of neural network, and comparing with the simple genetic algorithm and the particle swarm algorithm, the experimental results illustrate that the proposed algorithm has better search capability and higher stability than that of others.

关键词： quantum-particle swarm evolutionary algorithm quantum evolutionary algorithm particle swarm optimization function optimization neural network optimization

作者: LI Shiyong LI Hao

作者单位: Department of Control Science and Engineering, Harbin Institute of Technology Harbin, China

会议类型: 国际会议

会议名称: 2010 International Conference on Measurement and Control Engineering(2010年IEEE测量与控制工程国际会议 ICMCE2010)

会议地点: 成都

会议语种:英文

页码: 677-681

在线出版日期: 2010-11-16（万方平台首次上网日期，不代表论文的发表时间）