摘要
为了克服标准灰狼优化(GWO)算法寻优精度不高,难以在收敛速度和避免陷入局部最优之间取得平衡等问题,提出了一种改进的灰狼优化(IGWO)算法.该算法采用非线性收敛因子策略和自适应调整策略来提高寻优精度和加快收敛速度.选取10个基准函数对IGWO算法进行验证表明,IGWO算法的优化精度和收敛速度显著优于标准GWO算法和其他元启发式算法,因此本文提出的IGWO算法在求解最优参数方面具有良好的应用价值.
An improved grey wolf optimization(IGWO)algorithm is proposed to overcome the problems of low optimization accuracy of standard grey wolf optimization(GWO)algorithm,difficulty of balance between the convergence speed and local optimization.IGWO algorithm utilizes nonlinear convergence factor strategy and adaptive adjustment strategy to improve the optimization accuracy,accelerate the convergence speed.Thus,10 benchmark functions are selected to verify the IGWO algorithm.The results show that the optimization accuracy and convergence speed of the IGWO algorithm are significantly better than the standard GWO algorithm and particle swarm optimization algorithm.Consequently,the proposed IGWO algorithm in this paper exhibit positive application value in solving the optimal parameters.
作者
陈贞
闫明晗
CHEN Zhen;YAN Minghan(College of Mechatronics and Information Engineering,Putian University,Putian 351100,China;College of Electronic Information Engineering,Changchun University,Changchun 130022,China)
出处
《延边大学学报（自然科学版）》
CAS
2022年第3期250-254,共5页
Journal of Yanbian University（Natural Science Edition）
基金
福建省自然科学基金(2019J01814)
莆田学院校级科研项目(2022033)。
关键词
灰狼优化算法
线性收敛因子
自适应调整策略
元启发式算法
grey wolf optimization algorithm
nonlinear convergence factor
adaptive adjustment strategy
meta-heuristic algorithm
