Research on Improved Particle Swarm Algorithms and Its Application in Function Optimization

被引：0

作者：

Tang Chao-li ^{[1
]}

Huang You-rui ^{[1
]}

Qu Li-guo ^{[1
]}

Ling Liu-yi ^{[1
]}

机构：

[1] Anhui Univ Sci & Technol, Inst Elect & Informat Engn, Huainan, Anhui, Peoples R China

来源：

2011 INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND MULTIMEDIA COMMUNICATION | 2011年

关键词：

function optimization; particle swarm optimization; genetic algorithms;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The characteristics and deficiencies of traditional genetic for function optimization are discussed. Based on principle of particle swarm algorithm, and a improved particle swarm optimization algorithm(IPSOA) for function optimization is proposed. Finally, the algorithm is applied to the optimization of typical functions, The results show that the IPSOA has good convergence advantages, the parameter can be easily chosen, and the experiments have revealed its simplicity and effectiveness.

引用

页码：341 / 344

页数：4

共 7 条

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[3] Li Min-Qiang, 2002, Acta Automatica Sinica, V28, P497
[4] Liu Li-li, 2009, Control and Decision, V24, P1841
[5] Qing Lu, 2009, MOSHI SHIBIE YU RENG, V22, P91
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Wang, Yong-Jun
[J]. OPTIMIZATION METHODS & SOFTWARE, 2010, 25 (05): : 781 - 795
[7] An Improved Genetic Algorithm Based on Fixed Point Theory for Function Optimization
Zhang, Jingjun
Dong, Yuzhen
Gao, Ruizhen
Shang, Yanmin
[J]. 2009 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND TECHNOLOGY, VOL I, PROCEEDINGS, 2009, : 527 - +

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