中南大学学报（自然科学版）2018，Vol.49Issue(4)：857-864,8.DOI:10.11817/j.issn.1672-7207.2018.04.012
用对数函数描述收敛因子的改进灰狼优化算法及其应用
Improved grey wolf optimization algorithm with logarithm function describing convergence factor and its application
摘要
Abstract
The grey wolf optimization (GWO) algorithm has a few disadvantages such as low precision and high possibility of being trapped in local optimum, an improved GWO algorithm was proposed for solving high-dimensional optimization problem based on the convergence factor about logarithmic function. An initial population was generated based on good point set method to assure that the individuals were distributed in the search space as uniformly as possible. A nonlinear convergence factor was proposed based on logarithm function to balance the exploration ability and exploitation ability. Improved elite opposition-based learning strategy was used to avoid premature convergence of GWO algorithm. Benchmark functions and parameters optimization of real application were employed to verify the performance of the improved GWO algorithm. The results show that the proposed algorithm has better performance.关键词
灰狼优化算法/对数函数/收敛因子Key words
grey wolf optimization algorithm/logarithm function/convergence factor分类
信息技术与安全科学引用本文复制引用
伍铁斌,桂卫华,阳春华,龙文,李勇刚,朱红求..用对数函数描述收敛因子的改进灰狼优化算法及其应用[J].中南大学学报（自然科学版）,2018,49(4):857-864,8.基金项目
国家自然科学基金资助项目(61621062,61463009,61673400) （61621062,61463009,61673400）
湖南省自然科学基金青年基金资助项目(2016JJ3079) （2016JJ3079）
贵州省科学技术基金资助项目(黔科合基础[2016]1022) （黔科合基础[2016]1022）
娄底市科技计划项目(2017)(Projects(61621062, 61463009, 61673400) supported by the National Natural Science Foundation of China （2017）
Project(2016JJ3079) supported by the Youth Fund of the Natural Science Foundation of Hunan Province （2016JJ3079）
Project([2016]1022) supported by the Fund of Guizhou Science and Technology （[2016]1022）
Project(2017) supported by the Fund of Loudi Science and Technology) （2017）
