Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 409478, 20 pages
http://dx.doi.org/10.1155/2012/409478
Research Article

An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

Xiaohui Yan,1,2 Yunlong Zhu,1 Hao Zhang,1,2 Hanning Chen,1 and Ben Niu3

1Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2Graduate School of the Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100039, China
3College of Management, Shenzhen University, Shenzhen 518060, China

Received 2 April 2012; Revised 9 September 2012; Accepted 10 September 2012

Academic Editor: Elmetwally Elabbasy

Copyright © 2012 Xiaohui Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.