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

来源：

IETE TECHNICAL REVIEW | 2010年 / 27卷 / 04期

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

页码：336 / 348

页数：13

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