计算机应用研究2017，Vol.34Issue(12)：3603-3606,3610,5.DOI:10.3969/j.issn.1001-3695.2017.12.019
基于灰狼算法的改进研究
Study on improvement of gray wolf algorithm
郭振洲 1刘然 1拱长青 1赵亮1
作者信息
- 1. 沈阳航空航天大学计算机学院,沈阳110136
- 折叠
摘要
Abstract
According to gray wolf algorithm is easily trapped in local optimum and the convergence rate is not ideal,based on the improved convergence factor strategy and dynamic weighting strategy and two mixed strategies,this paper improved the wolf optimization algorithm and used to solve the function optimization problem.This paper proposed a nonlinear convergent factor formula,which could dynamically adjust the global searching ability of the algorithm,and introduced the dynamic weight to accelerate the convergence rate of the algorithm.15 benchmark test functions verified the global search ability and local search ability and convergence speed of the improved algorithm.The experimental results show that the improved algorithm is bettter than the standard wolf algorithm in terms of search ability and convergence rate.关键词
灰狼算法/收敛因子/动态权重/收敛速度Key words
gray wolf optimization(GWO) algorithm/convergence factor/dynamic weight/convergence rate分类
信息技术与安全科学引用本文复制引用
郭振洲,刘然,拱长青,赵亮..基于灰狼算法的改进研究[J].计算机应用研究,2017,34(12):3603-3606,3610,5.
