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