An Improved PSO Algorithm Based on Supervised Learning

被引：0

作者：

Gao Hui ^{[1
]}

Zhu Peiyi ^{[1
]}

Liu Yayan ^{[1
]}

Gao Jue ^{[1
]}

机构：

[1] Changshu Inst Technol, Sch Elect & Automat Engn, Changshu, Peoples R China

来源：

2011 INTERNATIONAL CONFERENCE ON FUTURE MANAGEMENT SCIENCE AND ENGINEERING (ICFMSE 2011), VOL 2 | 2011年 / 6卷

关键词：

Particle Swarm Optimization; Inertia Value; Neural Networks; Supervised Learning Algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This paper abandoned the idea that only concern of the inertia weight omega of PSO which is very important to set the inertia weight. By using supervised learning algorithm, this paper intends to develop an improved PSO algorithm, which adaptively adjusts the inertia value of every particle, and promotes the ability of global and local search. The experimental results show that the presented algorithm has the advantages of good global searching ability, convergence speed, accuracy and the stability.

引用

页码：41 / 46

页数：6

共 12 条

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