计算机应用研究2016，Vol.33Issue(12)：3648-3653,6.DOI:10.3969/j.issn.1001-3695.2016.12.029
一种新型非线性收敛因子的灰狼优化算法
Novel grey wolf optimization algorithm based on nonlinear convergence factor
摘要
Abstract
The classical grey wolf optimization (GWO)algorithm has a few disadvantages of low solving precision and high possibility of being trapped in local optimum.This paper proposed a novel grey wolf optimization (NGWO)algorithm for sol-ving unconstrained optimization problems.The proposed algorithm used opposition-based learning strategy to initiate popula-tion,which strengthened the diversity of global searching.Inspired by particle swarm optimization (PSO),this paper proposed an improved convergence factor update equation,which was based on that the values of parameter a are nonlinearly decreased over the course of iterations.The convergence factor was dynamically adjusted to maintain a better balance between global search and local search.Mutation operator was given on the current optimal individual of each generation,thus it could effec-tively jump out of local minima.Experiments are conducted on a set of 10 unconstrained benchmark functions.Based on the results,the proposed NGWO algorithm shows significantly better performance than the standard GWO algorithm.关键词
灰狼优化算法/反向学习策略/函数优化/非线性Key words
grey wolf optimization algorithm/opposition-based learning strategy/function optimization/nonlinear分类
信息技术与安全科学引用本文复制引用
王敏,唐明珠..一种新型非线性收敛因子的灰狼优化算法[J].计算机应用研究,2016,33(12):3648-3653,6.基金项目
国家自然科学基金资助项目（61403046）；湖南省科学计划资助项目 （）
