A study on bacterial colony chemotaxis algorithm and simulation based on differential strategy
Zhipeng Wang, Zhang Li, Yuwei Fan · International Journal of Modelling Identification and Control · 2010
Bacterial colony chemotaxis (BCC) algorithm is one of the new heuristic algorithms based on colony intelligence. The algorithm much improves the convergence speed of bacterial colony (BC) algorithm while possessing the strong searching ability of a single bacterium. To further enhance the ability of breaking away from the local optimum on multimodal function, two improvements are presented, one is adjusting the sense limit (SL) self-adaptively and the other is introducing differential evolutionary strategy into algorithm. Large amounts of numerical experiments simulation show that the performances of the improved BCC algorithm have been enhanced in success rate, convergence precision and the ability of breaking away from the local optimum. The improved BCC algorithm applied to substation planning problem which is a multi-constraint, multi-objective, large-scale and non-linear combinatorial optimisation problem has good performance of global convergence and better convergence speed.