[1]代永强,王联国.PSO和SFLA混合优化算法[J].计算机技术与发展,2014,24(04):77-79.
　DAI Yong-qiang,WANG Lian-guo.Hybrid Optimization Algorithm of PSO and SFLA[J].,2014,24(04):77-79.
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PSO和SFLA混合优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年04期
页码:
77-79
栏目:
智能、算法、系统工程
出版日期:
2014-04-30

文章信息/Info

Title:
Hybrid Optimization Algorithm of PSO and SFLA
文章编号:
1673-629X（2014）04-0077-03
作者:
代永强; 王联国
甘肃农业大学 信息科学技术学院
Author(s):
DAI Yong-qiang; WANG Lian-guo
关键词:
粒子群优化算法; 混合蛙跳算法; 混合算法; 优化性能
Keywords:
Particle Swarm Optimization ( PSO) algorithm; Shuffled Frog Leaping Algorithm ( SFLA); hybrid algorithm; optimization performance
分类号:
TP18
文献标志码:
A
摘要:
各种智能优化算法由于进化原理不同,优化性能各异,将不同种类的智能优化算法混合起来,往往能够取长补短,互相促进,提高混合算法的优化性能。利用粒子群优化(PSO)算法的快速收敛特性和混合蛙跳算法(SFLA)突出的全局协同搜索能力,提出了一种PSO-SFLA混合优化算法。该混合算法在执行过程中将种群分为2个子群体,一个子群体采用PSO算法进化寻优,另一个子群体采用改进的SFLA进化寻优,2个子群体共享整个种群极值信息。通过对3个标准函数进行实验并与基本PSO算法进行比较,实验结果表明混合算法获得了更好的解,具有更好的优化性能。
Abstract:
All kinds of intelligent optimization algorithm show different optimized performance because of different evolution principle. To mix different kinds of intelligent optimized algorithm can complement and promote each othera,and improve the optimization perform-ance of hybrid algorithm. A hybrid algorithm combined Particle Swarm Optimization ( PSO) and Shuffled Frog Leaping Algorithm ( SF-LA) is proposed by using the rapid convergence properties of PSO algorithm and the outstanding global cooperative search ability of SF-LA. The algorithm divides the swarm into two sub-groups. In each iteration,one sub-group evolves using PSO algorithm,the other sub-group evolves using SFLA,and two algorithms share the information of groups extremum. The algorithm is experimented for three stand-ard functions and compared with basic PSO algorithm,results show that PSO-SFLA hybrid algorithm outperforms PSO algorithm.

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更新日期/Last Update: 1900-01-01