粒子群优化算法研究进展<sup>*</sup>

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Abstract The particle swarm optimization (PSO) algorithm is an evolutionary algorithm that simulates the mechanism of biological swarm social behavior. The models of bird flocking and swarm actions are firstly introduced, and the fundamental characteristics and the working mechanisms of PSO algorithm are also analyzed. Then the recent progress in theory of PSO algorithm is reviewed, which are related to the improvement of PSO algorithm, the parameter selection in PSO algorithm, the convergence features of PSO algorithm, and the merging mechanism to other metaheuristic optimization algorithms. In addition, several typical application areas of PSO algorithm are surveyed respectively, which include continuous function optimization, neural network training, optimization of power system and optimization in electromagnetics. Finally, some suggestions on future trends and existing problems related to PSO algorithm are discussed and concluded.

Key words： Swarm Intelligence Particle Swarm Optimization (PSO) Optimization Problem

Received: 11 May 2006

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	NI QingJian
	XING HanCheng
	ZHANG ZhiZheng
	WANG ZhenZhen
	WEN JuFeng

Cite this article:

NI QingJian,XING HanCheng,ZHANG ZhiZheng等. Survey of Particle Swarm Optimization Algorithm[J]. , 2007, 20(3): 349-357.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2007/V20/I3/349

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