PQPSO:A New Parallel Quantum-Behaved Particle Swarm Optimization

摘要：

Quantum-behaved Particle Swarm Optimization is a new particle Swarm Optimization algorithm.Compared with Standard Particle Swarm Optimization (SPSO),it guarantees that particles converge in global optimum point in probability and this algorithm has better performance and stability.This paper based on Suns work 1,2 introduces an improved Quantum-behaved Particle Swarm Optimization Algorithm,multi-swarm Parallel Quantum-behaved Particle Swarm Optimization (PQPSO).In this algorithm,employs the co-evolution model to avoid pro-maturity and improve global search performance.This approach is tested on several accredited benchmark functions and the experiment results show much advantage of PQPSO to SPSO and QPSO,and the running time is also decreased in linear.

关键词： parallel quantum particle swarm co-evolution

作者: Yan Ma Yang Liu Yupin Chen Xiuzhen Li

作者单位: Department of Information Science and Technology,Taishan University,Taian,Shandong,271021,China Jiangsu Wuxi Institute of Communications Technology Department of Radiology,Taishan Medial University

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: 46-51

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）