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Adaptive Bacterial Foraging Optimization
Chen HN(陈瀚宁); Zhu YL(朱云龙); Hu KY(胡琨元)
Department工业信息学研究室
Source PublicationABSTRACT AND APPLIED ANALYSIS
ISSN1085-3375
2011
Issue0Pages:1-27
Indexed BySCI
WOS IDWOS:000298430000001
Contribution Rank1
Funding OrganizationThis paper is supported by the National 863 Plans Projects of China under Grants 2008AA04A105 and 2006AA04119-5, and the Natural Science Foundation of Liaoning Province under Grant 20091094.
KeywordChemotaxis Algorithm System
AbstractBacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run- length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run- length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
Language英语
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectMathematics, Applied ; Mathematics
WOS KeywordCHEMOTAXIS ; ALGORITHM ; SYSTEM
WOS Research AreaMathematics
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/7061
Collection工业信息学研究室
AffiliationKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114, Dongling District, Shenyang 110016, China
Recommended Citation
GB/T 7714
Chen HN,Zhu YL,Hu KY. Adaptive Bacterial Foraging Optimization[J]. ABSTRACT AND APPLIED ANALYSIS,2011(0):1-27.
APA Chen HN,Zhu YL,&Hu KY.(2011).Adaptive Bacterial Foraging Optimization.ABSTRACT AND APPLIED ANALYSIS(0),1-27.
MLA Chen HN,et al."Adaptive Bacterial Foraging Optimization".ABSTRACT AND APPLIED ANALYSIS .0(2011):1-27.
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