A Review of Quantum-behaved Particle Swarm Optimization

被引:118
作者
Fang, Wei [1 ]
Sun, Jun [1 ]
Ding, Yanrui [1 ]
Wu, Xiaojun [1 ]
Xu, Wenbo [1 ]
机构
[1] Jiangnan Univ, Sch Informat Technol, Wuxi 214122, Peoples R China
关键词
Evolutionary computation; Global optimization; Particle swarm optimization; Swarm intelligence; Quantum-behaved particle swarm optimization; Quantum system; NEURAL-NETWORK; COMBINATORIAL; ALGORITHM;
D O I
10.4103/0256-4602.64601
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm and it has attracted a large number of widespread researchers. As a branch of PSO, a probabilistic PSO algorithm, which is quantum-behaved PSO (QPSO), was proposed on the quantum mechanics and trajectory analysis of PSO. QPSO shines for its simplicity, easy implementation, and fine search ability. QPSO has also gained many researchers on its improvements and has been shown to offer good performance in a variety of applications. This paper attempts to give a compendious and timely review on QPSO by categorizing the publications on the improvements and applications.
引用
收藏
页码:336 / 348
页数:13
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