An improved PSO with time-varying accelerator coefficients

被引：42
作者：
Cui, Zhihua [1 ]
Zeng, Jianchao [1 ]
Yin, Weng [1 ]
机构：
[1] Taiyuan Univ Sci & Technol, Div Syst Simulat & Comp Applicat, Taiyuan 030024, Shanxi, Peoples R China
来源：
ISDA 2008: EIGHTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 2, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/ISDA.2008.86
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Cognitive and social learning factors are two important parameters of particle swarm optimization (PSO), and many different settings have been proposed, in which one famous strategy is the linear manner proposed by Ratnaweera. However, due to the complex nature of the optimization problems, linear-type setting may not work well in many cases. Since the large cognitive coefficient provides a large local search capability, as well as the small one employs a large global search capability, three different non-linear settings are designed to further investigate the potential advantages among these two parameters. Simulation results show the concave function strategy is an effective manner especially for multi-modal functions.
引用
收藏
页码：638 / 643
页数：6
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