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. 1. Introduction Bacteria foraging algorithm is one type of bioinspired optimization algorithm that is gaining popularity due to its capability in dealing with numerous real world applications [1]. The strategy is based on Escherichia coli (E. coli) bacteria behaviour to find nutrient or food source during their lifetime which consists of several phases. The most prominent phase that determines the performance of the algorithm is the chemotaxis mechanism through random tumble and swim actions. In this phase, the bacteria movement can be made faster if they move with a large step from one location to another location. However, the risk of this option is that the bacteria may be unable to locate the optimum food source location if it is positioned in a remote area. From an optimization point of view, this results in faster convergence but low accuracy. On the contrary, the optimum food source location might be easily found if the bacteria are moving with smaller step size. Nevertheless, with this choice, bacteria require more time and more steps need to be considered in order to reach the optimum food source. In other words, the convergence speed of the algorithm is slower but high accuracy can be achieved. In order to overcome such problems, variation of bacteria step size throughout the search operation might be introduced. This can be realized through adaptation of a certain relationship such as using mathematical formulation
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