摘要
Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is proposed. Specific improvements includes:(1)An adaptive nonlinear inertia weight based on Branin function was introduced to balance global search and local mining.(2) A mirror selection method is proposed to improve the individual quality and speed up the convergence. By optimizing several test functions and comparing the experimental results with other three algorithms,this study verifies that WOA-MS has an excellent optimization performance.
针对鲸鱼优化算法收敛速度慢、精度低、易陷入局部最优解的缺点,提出了一种基于镜像选择的改进鲸鱼优化算法(Whale optimization algorithm based-on mirror selection,WOA-MS)。具体改进包括:(1)为了平衡全局搜索和局部开采,提出了一种基于Branin函数的自适应非线性惯性权重;(2)为了提高算法的个体质量和收敛速度,提出了一种镜像选择方法。通过对若干种测试函数进行优化,并与其他三种算法的实验结果进行比较,证明了WOA-MS具有良好的优化性能。
基金
supported by the Natural Science Foundation of Jiangsu Province (No. BK20151479)
the Open Foundation of Graduate Innovation Base in Nanjing University of Aeronautics and Astronautics(No. kfjj20190736)
