基于单纯形法的蝙蝠算法

Bat Algorithm Based on Simplex Method

针对蝙蝠算法局部搜索能力低、迭代后期收敛速度较慢的缺陷,提出基于单纯形法的蝙蝠算法。该算法对进入下一次迭代前对部分较差个体采用单纯形法的扩张、收缩/压缩操作,提高局部搜索能力,进而提高算法的寻优能力。对6个CEC2005 benchmark测试函数进行测试比较,仿真结果表明,改进算法的收敛速度、收敛精度、鲁棒性等寻优性能明显优于基本的蝙蝠算法和参考文献对比算法。

Bat algorithm based on simplex method was presented to overcome the poor capacity in local searching and low speed of convergence of bat algorithm. The algorithm uses the expansion and contraction / compression operation of simplex method to enter the next iteration of individuals to enhance the capacity of global optimization,and to improve the convergence speed. The six CEC2005 benchmark functions were tested and compared and the simulation results show that the proposed algorithm has the advantages of better global searching ability,faster convergence and more precise convergence than those of the basic bat algorithm and the reference comparison algorithm.