Contraction-Expansion Coefficient Learning in Quantum-Behaved Particle Swarm Optimization

摘要：

Quantum-behaved particle swarm optimization was proposed from the view of quantum world and based on the particle swarm optimization, which has been proved to outperform the traditional PSO. The Expansion-Contraction coefficient is the only parameter in QPSO, which has great influence on the global search ability and convergence of the particles. In this paper, two parameter control methods are proposed. Numerical experiments on the benchmark functions are presented.

关键词： Quantum-behaved Particle Swarm Optimization Contraction-Expansion coefficient cosine function annealing function

作者: Na Tian Choi-Hong Lai Koulis Pericleous Jun Sun Wenbo Xu

作者单位: School of Computing and Mathematical ScienceUniversity of GreenwichLondon, UK School of IOT Engineering Jiangnan University Wuxi, China

会议类型: 国际会议

会议名称: 2011 IEEE 10th International Symposium on Distributed Computing and Applications to Business,Engineering(第十届电子商务、工程及科学领域的分布式计算和应用国际学术研讨会 DCABES 2011)

会议地点: 无锡

会议语种:英文

页码: 303-308

在线出版日期: 2011-10-14（万方平台首次上网日期，不代表论文的发表时间）