Cooperative Approaches to Bacterial Foraging Optimization

Authors:
Hanning Chen;Yunlong Zhu;Kunyuan Hu;Xiaoxian He;Ben Niu
Affiliations:
Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 110016 and School of Graduate, Chinese Academy of Sciences, Beijing, China ...;Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 110016;Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 110016;College of Information Science & Engineering, Central South University, Changsha, China 410083;Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 110016 and School of Graduate, Chinese Academy of Sciences, Beijing, China ...
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the foraging behavior of E. colibacteria. This paper presents a variation on the original BFO algorithm, called Cooperative Bacterial Foraging Optimization (CBFO), which significantly improve the convergence speed, accuracy and robustness of original BFO in solving complex optimization problems. This is achieved by applying two cooperative approaches to the BFO, namely the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. Four widely-used benchmark functions have been implemented to test the proposed algorithm, and the results show remarked improvement in performance over the original BFO.