摘要
针对原始灰狼优化算法(GWO)易陷入局部最优和全局搜索能力较弱的问题,提出一种改进灰狼优化算法(Improved Grey Wolf Optimization,IGWO)。该算法首先通过引入非线性收敛因子来调整控制参数,进一步平衡GWO的全局搜索和局部开发能力。其次,在灰狼位置更新的过程中结合布谷鸟优化算法的搜索机制,帮助灰狼种群陷入停滞时跳出局部最优。最后,在6个基准测试函数进行仿真实验,结果表明IGWO能提升GWO的性能。
An Improved Grey Wolf Optimization(IGWO)is proposed to solve the problem that the original Grey Wolf Optimization(GWO)is easy to fall into local optimal and has weak global searching ability.Firstly,this algorithm introduces the nonlinear convergence factor to adjust the control parameter to further balance the global search and local development capabilities of GWO.Secondly,the search mechanism of Cuckoo Optimization algorithm is combined in the process of Grey Wolf position updating to help the Grey Wolf population to jump out of the local optimal.Finally,the simulation experiments on 6 benchmark test functions show that IGWO can improve the performance of GWO.
作者
陈建东
聂斌
雷银香
CHEN Jiandong;NIE Bin;LEI Yinxiang(College of Computer Science,Jiangxi University of Chinese Medicine,Nanchang 330004,China)
出处
《现代信息科技》
2023年第19期94-98,共5页
Modern Information Technology
基金
江西中医药大学星火传承培养计划(2252000111/4004)
江西省教育厅科学技术项目(GJJ2200925)
江西省中医药管理局科技计划项目(2020B0412)。
关键词
灰狼优化算法
非线性收敛因子
布谷鸟搜索
基准测试函数
Gray Wolf Optimization
nonlinear convergence factor
Cuckoo Search
benchmark test function
