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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 494271, 7 pages
http://dx.doi.org/10.1155/2014/494271
Research Article

Novel Adaptive Bacteria Foraging Algorithms for Global Optimization

Department of Automatic Control & Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK

Received 5 August 2013; Revised 11 February 2014; Accepted 20 February 2014; Published 25 March 2014

Academic Editor: Cheng-Jian Lin

Copyright © 2014 Ahmad N. K. Nasir 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

This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system.