一种优化局部搜索能力的灰狼算法  

A gray wolf algorithm with optimized local search ability

作　　者：王习涛 Wang Xitao(Data management center,Statistics Bureau of Henan province,Zhengzhou,Henan 450018,China)

机构地区：[1]河南省统计局数据管理中心,河南郑州450018

出　　处：《计算机时代》2020年第12期53-55,共3页Computer Era

摘　　要：灰狼算法是一种经典的群智能算法。针对灰狼算法局部搜索收敛较慢,易陷入局部最优且搜索精度不高的缺陷,提出一种新的灰狼算法。新算法通过增加α狼视野,赋予α狼主动搜索能力,在全局搜索的同时进一步提高了局部寻优精度,实现搜索结果的进一步优化。通过多个基准函数测试实验、对比、分析发现,新算法较原始灰狼算法在收敛精度上有较大提高。Gray wolf algorithm is a classical swarm intelligence algorithm.In this paper,a new gray wolf algorithm is proposed to solve the problems of gray wolf algorithm,which is easy to fall into local optimum,slow convergence and low search accuracy.In the new algorithm,by increasing alpha wolf's field of vision and giving alpha wolf the ability of active search,the local search accuracy is further improved and the search results are further optimized at the same time of global search.Compared with the original gray wolf algorithm,the new algorithm greatly improves the speed and accuracy of convergence according to the experimental test on several reference functions.

关 键 词：群智能算法 灰狼算法 优化 局部搜索 精度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]