一种引入 NeIder-Mead算子的改进狼群搜索算法

摘要

为了克服狼群搜索算法（WSA）存在的不足，提出一种新的混合优化算法，称之为引入Nelder-Mead算子的改进狼群搜索算法。该算法使得每只狼在个体搜索中能够利用群体信息和个体记忆来指导其搜索猎物，以提高算法的全局搜索能力；让每只狼在个体搜索中可使用Nelder-Mead方法，以弥补WSA在局部搜索能力上的不足。选取六个基准函数，用来测试算法的优化性能。实验结果表明：该算法能够寻得更优的最优解，且鲁棒性更强，可应用于求解高维复杂优化问题。%Aiming at overcoming the shortcoming of the wolf search algorithms (WSA),this paper presented a new hybrid op-timization algorithm,which was called an improved wolf search algorithm using Nelder-Mead operator.In this optimization algo-rithm,on one hand,each wolf could use the group information and individual memory to guide its searching for prey,so as to improve the global search ability of algorithm.On the other hand,each wolf could use Nelder-Mead method in its search process,so as to improve the local search ability of the individual and making up for the deficiency of local search ability about the WSA.In order to test the performance of the proposed algorithm,It conducted experiments on six benchmark functions,and the results show that the proposed algorithm can find out better optimum,has a stronger robustness,and it can be used to solve the high dimensional and complex optimization problems.