Solving Constrained Optimization Problems with Adaptive Quantum-Behaved Particle Swarm Optimization

摘要：

In this paper we propose a new algorithm in solving constrained problem-Adaptive Quantum-behaved Particle Swarm Optimization (AQPSO).The AQPSO outperforms QPSO and PSO in global search ability and local search ability,because the adaptive method is more approximate to the learning process of social organism with high-level swarm intelligence and can make the population evolve persistently.We adopt a non-stationary multi-stage assignment penalty in solving constrained problem to improve the convergence and gain more accurate results.This approach is tested on several accredited benchmark functions and the experiment results show much advantage of AQPSO to QPSO and the traditional PSO.

关键词： constrained adaptive quantum PSO multi-stage

作者: Yang Liu Yan Ma Baoxiang Cao Deyun Yang

作者单位: Department of Information Science and Technology,Taishan University,Taian Shandong,271021,China Col Department of Information Science and Technology,Taishan University,Taian Shandong,271021,China College of Computer Science,Qufu Normal University,Rizhao Shandong,276826,China

会议类型: 国际会议

会议名称: 2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会(2008 International Symposium on Distributed Computing and Applications for Business Engineering and Science)

会议地点: 大连

会议语种:英文

页码: 649-653

在线出版日期: 2008-07-27（万方平台首次上网日期，不代表论文的发表时间）