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Simulation and Study of Self-Adaptive Bacterial Colony Chemotaxis Algorithm

Bacterial colony chemotaxis (BCC) algorithm is a new colony intelligence optimization algorithm. In this paper through a mass of experiments on the standard test function, the impact of the algorithm parameters on the performance of algorithm is demonstrated, and then the parameter control strategies are given, which lay the foundation for further study of the algorithm. In older to enhance the success rate of BCC algorithm on multi-modal function further, two improvements are presented, one is adjusting the sense limit (SL) self-adaptively, and the other is introducing differential evolutionary strategy into BCC algorithm. The numerical experiment's results using Matlab show that the performances of the improved BCC algorithm have been enhanced both in success rate and convergence precision. Finally the algorithm is applied to the optimal planning of substation locating, and achieves the satisfactory results.