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混合群智能算法在模体识别中的应用
引用本文:杨柳,刘铁英,李雪莲.混合群智能算法在模体识别中的应用[J].长春邮电学院学报,2012(1):56-59.
作者姓名:杨柳  刘铁英  李雪莲
作者单位:[1]吉林工商学院信息工程分院,长春130062 [2]长春职业技术学院信息分院,长春130033 [3]吉林省财政厅吉林省财税信息中心,长春130021
基金项目:吉林省教育厅“十二五”科学技术研究基金资助项目(吉教科合字[2012]第371号)
摘    要:为了避免传统吉布斯算法的诸多缺陷,提高算法的求解能力,对蚁群算法(ACO:Ant Colony Optimiza-tion)进行了改进:引入粒子群算法(PSO:Particle Swarm Optimization)动态调节ACO函数中的参数获得最优解。在奔腾PC机的实验平台上、Windows 2003Server操作系统下、开发工具为VB的模拟实验中,结果证明,混合的群智能算法使经典旅行商问题求解的计算时间缩短,提高了算法的收敛速度,有较好的发展前景。利用PSO处理连续优化问题的优点,将混合算法应用于生物信息学的模体识别中,可实现更加快速的基序发现处理。

关 键 词:吉布斯算法  粒子群算法  模体识别

Application of Hybrid Swarm Intelligence Alogrithm on Finding Motif Problem
YANG Liu,LIU Tie-ying,LI Xue-lian.Application of Hybrid Swarm Intelligence Alogrithm on Finding Motif Problem[J].Journal of Changchun Post and Telecommunication Institute,2012(1):56-59.
Authors:YANG Liu  LIU Tie-ying  LI Xue-lian
Affiliation:1. Department of Information Engineering,Jilin Business and Technology College, Changchun 130062, China; 2. School of Information Technology, Changchun Vocational Institute of Technology, Changchun 130033,China; 3. Jilin Taxation Information Center, Jilin Provincial Finance Department, Changchun 130021, China)
Abstract:In order to avoid many Gibbs algorithm defects, improve the ability of problem solving, im- provements the ACO (Ant Colony Optimization) :PSO (Particle Swarm Optimization) is made to opti- mize the parameters in the ACO. Pentium PC machine is the experiment platform, operating system is Windows 2003 Server, development tools is VB, the traveling salesman problem is tsimalated. Results show that the computing time of the algorithm can be reduced by new methods. It had great effects in practicality and rapid processing of motif discovary.
Keywords:Gibbs algorithn  particle swarm optimization  finding motif
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