[1]崔长彩,李兵,张认成.粒子群优化算法[J].华侨大学学报(自然科学版),2006,27(4):343-347.[doi:10.3969/j.issn.1000-5013.2006.04.002]
　Cui Changcai,Li Bing,Zhang Rencheng.Particle Swarm Optimization[J].Journal of Huaqiao University(Natural Science),2006,27(4):343-347.[doi:10.3969/j.issn.1000-5013.2006.04.002]
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第27卷
期数:
2006年第4期
页码:
343-347
栏目:
出版日期:
2006-10-20

文章信息/Info

Title:
Particle Swarm Optimization
文章编号:
1000-5013(2006)04-0343-05
作者:
崔长彩; 李兵; 张认成
华侨大学机电及自动化学院; 华侨大学机电及自动化学院 福建泉州362021; 福建泉州362021
Author(s):
Cui Changcai;  Li Bing;  Zhang Rencheng
College of Mechanical Engineering and Automation, Huaqiao University, 362021, Quanzhou, China
关键词:
粒子群;  优化算法;  遗传算法;  惯性权重
Keywords:
particle swarm;  optimization algorithm;  genetic algorithm;  inertia weigth
分类号:
TP301.6
DOI:
10.3969/j.issn.1000-5013.2006.04.002
文献标志码:
A
摘要:
论述粒子群优化算法(PSO)的基本原理、特点、实现步骤,以及PSO的各种改进技术,包括基于PSO参数的改进技术(主要是惯性权重)、基于遗传算法进化机理的改进技术(受遗传算法启发提出的带交叉算子的PSO、带变异算子的PSO、带选择算子的PSO),以及其他算法融合的改进技术(模拟退火PSO、免疫PSO、混沌PSO),并总结PSO热点研究问题.
Abstract:
The particle swarm optimization(PSO) was introduced about its fundamentals,characteristics,implementation steps,and its improved versions,which were based on the parameter updating(mainly inertia weight),or illuminated by the evolutionary principles of genetic algorithm(GA)(e.g. PSOs with crossover,mutation or selection operator),or combined with other algorithms(e.g.simulating annealing PSO,immune PSO,chaotic PSO), and the hot topics of PSO were summarized too.

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备注/Memo

备注/Memo:
福建省青年科技人才创新基金资助项目(2005J030)
更新日期/Last Update: 2014-03-23