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Novel Bacterial Foraging Optimization with Time-varying Chemotaxis Step
Recently, bacterial foraging optimizer (BFO) has emerged as a powerful technique for optimization problem solving. However, various simulation results obtained from previous studies suggested that the performance of BFO depends heavily on the chemotaxis step length in in silico study of the optimization problem. In this paper, two modifications were proposed to introduce a linear variation and a nonlinear variation of chemotaxis step in order to improve the speed of convergence as well as fine tune the search in the multidimensional space. To illustrate the efficiency of the proposed algorithms (BFO-LDC and BFO-NDC), eight different benchmark functions were selected as testing functions to compare with original BFO and GA. Analysis of variance (ANOVA) test was also carried out to validate the efficacy of the proposed algorithms. Results of the comparison indicated that two proposed algorithms generally outperform classical BFO and GA in all the benchmark functions.
Bacterial foraging; Chemotaxis.
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