A Modified Particle Swarm Optimization Algorithm Based on Improved Chaos Search Strategy

被引：2

作者：

Gao, Xue-yao ^{[1
]}

Sun, Li-quan ^{[1
]}

Zhang, Chun-xiang ^{[2
]}

Yang, Shou-ang ^{[1
]}

机构：

[1] Harbin Univ Sci & Technol, Res Inst Comp Appl Tech, Harbin 150080, Peoples R China

[2] Harbin Univ Sci & Technol, Sch Software, Harbin 150080, Peoples R China

来源：

PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 1 | 2008年

关键词：

D O I：

10.1109/ISCID.2008.151

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Particle swarm optimization (PSO) algorithm is frequently employed to solve various optimization problems, but it easily gets into the local extremum in later evolution period. An improved chaos search strategy is introduced into PSO algorithm. When particles get into the local extremum, they are activated by chaos search strategy, and chaos search area are controlled in the neighborhood of the current optimal solution by reducing search area of variables, which avoids searching blindly. The new algorithm can not only solve local extremum problem effectively but also enhance the precision of convergence. Experiment results show that the proposed method is better than standard PSO algorithm in both precision and stability.

引用

页码：331 / +

页数：3

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