Particle Swarm Optimization Algorithm for Solving Optimization Problems

被引：22

作者：

Ozsaglam, M. Yasin ^{[1
]}

Cunkas, Mehmet ^{[2
]}

机构：

[1] Selcuk Univ, Bozkir MYO Bilgisayar Bolumu Bozkir, Konya, Turkey

[2] Selcuk Univ, Tekn Egitim Fak Elekt & Bilgisayar Egitim, TR-42003 Konya, Turkey

来源：

JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI | 2008年 / 11卷 / 04期

关键词：

Particle Swarm Optimization; Genetic Algorithms; Differential Evolution Algorithm;

D O I：

10.2339/2008.11.4.299-305

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Many optimization techniques used in solving optimization problems has been developed by inspiring from the events in nature. Particle Swarm Optimization (PSO) algorithm is a population based optimization technique inspired by social behavior of bird flocking and fish schooling. In this paper, PSO is compared with Genetic Algorithms and Differential Evolution Algorithm by using test functions. The results show that the PSO, in most problems, is able to find much better solutions and better convergence compared to other two algorithms.

引用

页码：299 / 305

页数：7

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