A Multi-Phased Quantum-Behaved Particle Swarm Optimization algorithm
Quantum-behaved Particle Swarm Optimization(QPSO) is a novel variant of particle Swarm Optimization algorithm. In contrast to Standard Particle Swarm Optimization (SPSO), QPSO guarantees that particles converge to global optimum point in probability and the algorithm has better performance. This paper based on Suns work 1, 2introduces an improved Quantum-behaved Particle Swarm Optimization, Multi-Phased QPSO (MQPSO),in which the swarm evolves with multi-swarm and multi-phase. It avoids particle pre-maturity and improves global search performance. The results of several important test functions confirm that the convergent performance of MQPSO outperforms PSO and QPSO in general.
PSO QPSO Global Convergence Premature
Wenbo Xu Chunyan Zhang
School of Information Technology, Southern Yangtze University, Wuxi Jiangsu 214122,China
国际会议
杭州
英文
524-527
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）