Self-Adaptation in Bacterial Foraging Optimization Algorithm

被引:28
作者
Chen, Hanning [1 ]
Zhu, Yunlong [1 ]
Hu, Kunyuan [1 ]
机构
[1] Chinese Acad Sci, Shenyang Inst Automat, Key Lab Ind Informat, Shenyang 110016, Peoples R China
来源
2008 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM AND KNOWLEDGE ENGINEERING, VOLS 1 AND 2 | 2008年
关键词
D O I
10.1109/ISKE.2008.4731080
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This paper first analyzes how the run-length unit parameter controls the exploration and exploitation ability of BFO, and then presents a variation on the original BFO algorithm, called the Self-adaptive Bacterial Foraging Optimization (SA-BFO), employing the adaptive search strategy to significantly improve the performance of the original algorithm. This is achieved by enabling SA-BFO to adjust the run-length unit parameter dynamically during evolution to balance the exploration/exploitation tradeoff. Application of SA-BFO on several benchmark functions shows a marked improvement in performance over the original BFO.
引用
收藏
页码:1026 / 1031
页数:6
相关论文
共 8 条
[1]  
CHEN HN, APPL MATH COMP UNPUB
[2]   The particle swarm - Explosion, stability, and convergence in a multidimensional complex space [J].
Clerc, M ;
Kennedy, J .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (01) :58-73
[3]  
Kim DH, 2005, LECT NOTES COMPUT SC, V3528, P231
[4]   A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation [J].
Mishra, S .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2005, 9 (01) :61-73
[5]  
Passino KM, 2002, IEEE CONTR SYST MAG, V22, P52, DOI 10.1109/MCS.2002.1004010
[6]  
Shi Y., 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), P1945, DOI 10.1109/CEC.1999.785511
[7]  
Sumathi S., 2008, EVOLUTIONARY INTELLI
[8]  
Tripathy M, 2006, LECT NOTES COMPUT SC, V4193, P222