Novel Adaptive Bacteria Foraging Algorithms for Global Optimization. (25th March 2014)

• Record Type: Journal Article
• Authors: Nasir, Ahmad N. K.; Tokhi, M. O.; Ghani, N. Maniha Abd.
• Other Names: Lin Cheng-Jian Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2014(2014)
• Publication Date: 2014-03-25
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2014/494271
• Languages: English
• ISSNs: 1687-9724

Abstract

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.