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Improved bacterial foraging optimization with social cooperation and adaptive step size
Yan XH(晏晓辉); Zhu YL(朱云龙); Chen HN(陈瀚宁); Zhang H(张浩)
Department信息服务与智能控制技术研究室
Conference Name8th International Conference on Intelligent Computing Technology, ICIC 2012
Conference DateJuly 25, 2012 - July 29, 2012
Conference PlaceHuangshan, China
Author of SourceIEEE Computational Intelligence Society; International Neural Network Society; National Science Foundation of China
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Publication PlaceHeidelberg, Germany
2012
Pages634-640
Indexed ByEI
EI Accession number20123515376063
Contribution Rank1
ISSN0302-9743
ISBN978-3-642-31587-9
KeywordAlgorithms Biochemistry Intelligent Computing
AbstractThis paper proposed an Improved Bacterial Foraging Optimization (IBFO) algorithm to enhance the optimization ability of original Bacterial Foraging Optimization. In the new algorithm, Social cooperation is introduced to guide the bacteria tumbling towards better directions. Meanwhile, adaptive step size is employed in chemotaxis process. The new algorithm is tested on a set of benchmark functions. Canonical BFO, Particle Swarm Optimization and Genetic Algorithm are employed for comparison. Experiment results show that the IBFO algorithm offers significant improvements over the original BFO algorithm and is a competitive optimizer for numerical optimization. © 2012 Springer-Verlag.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/9883
Collection信息服务与智能控制技术研究室
Affiliation1.Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, China
2.Graduate School, Chinese Academy of Sciences, 100039, Beijing, China
Recommended Citation
GB/T 7714
Yan XH,Zhu YL,Chen HN,et al. Improved bacterial foraging optimization with social cooperation and adaptive step size[C]//IEEE Computational Intelligence Society; International Neural Network Society; National Science Foundation of China. Heidelberg, Germany:Springer Verlag,2012:634-640.
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