Bacterial foraging group-tours algorithm based on differential evolution
基于差分进化的细菌觅食群游算法*

In view of the defects of the same swim step and slow velocity in the bacterial foraging algorithm, this paper gave bacteria the ability of context-aware, and increased the convergence speed using the sensitivity to adjust the group swim step. introduced the ideas of differential evolution to the process of chemotaxis to optimize the bacterial position in the process of amendment, and improved the degradation dimension in the process of group tour and increased the accuracy of convergence. It tested the algorithm by the high-dimensional and multimodal function. New algorithm significantly improved the search speed and accuracy, and it is suitable for practical engineering problems of multi-dimensional, constrained optimization.