基于混沌搜索的混和粒子群优化算法

山东大学学报(工学版)

基于混沌搜索的混和粒子群优化算法

张劲松¹，李歧强¹，王朝霞²

1. 山东大学控制科学与工程学院，山东济南 250061；2. 山东轻工业学院电子信息与控制工程学院，山东济南 250353

收稿日期:2005-12-29 修回日期:1900-01-01 出版日期:2007-02-24 发布日期:2007-02-24
通讯作者: 张劲松

Hybrid particle swarm optimization algorithm based on the chaos search

ZHANG Jin-song¹, LI Qi-qiang¹, WANG Zhao-xia²

1. School of Control Science and Engineering, Shandong University, Jinan 250061, China; 2. College of Electronic Information and Control Engineering, Shandong Institute of Light Industry, Jinan 250353, China

Received:2005-12-29 Revised:1900-01-01 Online:2007-02-24 Published:2007-02-24
Contact: ZHANG Jin-song

摘要/Abstract

摘要： 所提出的算法将粒子群优化算法和混沌算法相结合，既摆脱了算法搜索后期易陷入局部极值点的缺点，同时又保持了前期搜索的快速性.最后通过4个测试函数将该算法与基本粒子群算法进行仿真对比，比较结果表明基于混沌搜索的混和粒子群优化算法在收敛性和稳定性等方面明显优于基本粒子群优化算法.

关键词: 粒子群优化算法, 混沌搜索, 混和算法, 遍历性, 局部极值

Abstract: A hybrid particle swarm optimization algorithm based on the chaos search is proposed. It can not only overcome the disadvantage of easily getting into the local extremum in the later evolution period, but also keep the rapidity of the previous period. Finally, the basic particle swarm optimization algorithm is compared with the hybrid algorithm. The experiment results demonstrate that the new algorithm proposed is better than the basic particle swarm optimization algorithm in the aspects of convergence and stability.

Key words: chaos search, hybrid algorithm, ergodicity, local extremum , particle swarm optimization algorithm

中图分类号:

TP301.6

张劲松,李歧强,王朝霞 . 基于混沌搜索的混和粒子群优化算法[J]. 山东大学学报(工学版), 2007, 37(1): 47-50 .

ZHANG Jin-song,LI Qi-qiang,WANG Zhao-xia . Hybrid particle swarm optimization algorithm based on the chaos search[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(1): 47-50 .

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