alexa A Novel Strategy Adaptation Based Bacterial Foraging Al
ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
Open Access

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Research Article

A Novel Strategy Adaptation Based Bacterial Foraging Algorithm for Numerical Optimization

Chin-Ling Lee1 and Cheng-Jian Lin2*

1Department of International Trade, National Taichung University of Science and Technology, Taichung City 404, Taiwan

2Department of Computer Science and Information Engineering National Chin-Yi University of Technology, Taichung City 41170, Taiwan

Corresponding Author:
Cheng-Jian Lin
Department of Computer Science and Information Engineering
National Chin-Yi University of Technology
Taichung City 41170, Taiwan
Tel: 886-4-23924505; E-mail:[email protected]

Received date: December 29, 2015; Accepted date: January 19, 2016; Published date: January 25, 2016

Citation: Lee CL, Lin CJ (2016) A Novel Strategy Adaptation Based Bacterial Foraging Algorithm for Numerical Optimization. Int J Swarm Intel Evol Comput 5:128. doi:10.4172/2090-4908.1000128

Copyright: © 2016 Lee CL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

In this paper, a strategy-adaptation-based bacterial foraging optimization (SABFO) algorithm is proposed to
solve the optimization of complex problems. The proposed SABFO algorithm adopts the strategic approach into
chmotaxis step of traditional bacterial foraging optimization (BFO). The proposed method makes each bacterium
swim on different run-lengths, and increases bacterial diversity as well. Five optimization problems of nonlinear
benchmark functions are used to verify the performance of SABFO. Simulation results show that the SABFO obtains
better global optimal solutions than other methods.

Keywords

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