[1]赵春丽,刘 清.基于微粒群策略的自适应觅食算法研究[J].南京师范大学学报(工程技术版),2013,13(01):050.
　Zhao Chunli,Liu Qing.Self-Adaptive Bacterial Foraging Algorithm Based on Particle Swarm Optimization Strategy[J].Journal of Nanjing Normal University(Engineering and Technology),2013,13(01):050.
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基于微粒群策略的自适应觅食算法研究
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
13卷
期数:
2013年01期
页码:
050
栏目:
出版日期:
2013-03-31

文章信息/Info

Title:
Self-Adaptive Bacterial Foraging Algorithm Based on Particle Swarm Optimization Strategy
作者:
赵春丽; 刘 清
南京师范大学计算机科学与技术学院,江苏 南京 210023
Author(s):
Zhao Chunli; Liu Qing
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
关键词:
BFA算法; 收敛速度; 自适应趋化步长; PSO算法
Keywords:
BFA algorithm; convergence rate; self-adaptive chemotactic step; PSO algorithm
分类号:
TP301
摘要:
为了克服传统觅食算法BFA(Bacterial Foraging Algorithm)收敛速度慢以及高维优化收敛性差的问题,提出了一种新的基于微粒群优化策略的自适应觅食算法ABF-PSO(Adaptive Algorithm Bacterial Foraging Oriented by PSO).该算法采用自适应趋化步长来提高搜索能力,并根据微粒群优化PSO(Particle Swarm Optimization)策略来控制细菌的运动方向,避免了细菌运动方向因随机性选取而延误全局最优值搜索的问题.在详细阐述了动态调整细菌的趋化步长和利用微粒群优化策略更新细菌运动方向后,对经典测试函数分别采用PSO算法,BFA算法和ABF-PSO算法进行了对比测试.实验结果表明,ABF-PSO算法不仅收敛速度得到很大提高,同时对于复杂和高维搜索的问题获得了很好的收敛性.
Abstract:
To overcome the problems of low convergence rate,and poor convergence characteristics for larger constrained problems in conventional bacterial foraging algorithm(BFA),this paper proposes a new self-adaptive algorithm bacterial foraging based on PSO(ABF-PSO).The new algorithm improves the search ability with self-adaptive chemotactic steps,and controls the bacterial movement directions according to particle swarm optimization strategy,thus avoiding a delay in reaching the global solution because of random selection of the bacterial movement directions.After the detailed illustrations of dynamic adjustment bacterial chemotactic step,and updating bacterial movement directions by the velocity formula of PSO,this paper tests some classical functions with PSO algorithm,BFA algorithm,and ABF-PSO algorithm.The results show that ABF-PSO algorithm not only has greater improvement in convergence rate,but also gets a fruitful achievement in searching complex and high-dimensioned problems.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-01-14.
基金项目:国家自然科学基金(61103185)、江苏省高校自然科学
基金项目(10KJD520004).
通讯联系人:刘清,博士,教授,研究方向:面向移动物联网环境的搜索关键技术研究、蚁群算法研究.E-mail:njnulq@163.com
更新日期/Last Update: 2013-03-31