CHEN Zhen,YAN Minghan.An improved grey wolf optimization algorithm[J].Journal of Yanbian University,2022,(03):250-254.
一种改进的灰狼优化算法
- Title:
- An improved grey wolf optimization algorithm
- 文章编号:
- 1004-4353(2022)03-0250-05
- Keywords:
- grey wolf optimization algorithm; nonlinear convergence factor; adaptive adjustment strategy; meta - heuristic algorithm
- 分类号:
- TP301.6
- 文献标志码:
- A
- 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.
- Abstract:
- 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.
参考文献/References:
[1] MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software, 2014,69(3):46 - 61.
[2] BIDAR M, KANAN H R, MOUHOUB M, et al.Mushroom reproduction optimization(MRO): a novel nature - inspired evolutionary algorithm[C]//Proceedings of 2018 IEEE Congress on Evolutionary Computation(CEC).Rio de Janeiro, Brazil: IEEE, 2018:1762 - 1771.
[3] KARABOGA D, BASTURK B.A powerful and efficient algorithm for numerical function optimization: artificial bee colony(ABC)algorithm[J].Journal of Global Optimization, 2018,39(3):459 - 471.
[4] RAO R V, SAVSANI V J, VAKHARIA D P.Teaching learning - based optimization:a novel method for constrained mechanical design optimization problems[J].Computer Aided Design, 2011,43(3):303 - 315.
[5] HEIDARI A A, MIRJALILI S, FARIS H, et al.Harris hawks optimization: algorithm and applications[J].Future Generation Computer Systems, 2019,97:849 - 872.
[6] 游晓明,刘升,吕金秋.一种动态搜索策略的蚁群算法及其在机器人路径规划中的应用[J].控制与决策,2017,32(3):552 - 556.
[7] 杜利敏,陈河山,徐扬,等.基于ReliefF和聚类的特征选择方法及其在无线电信号识别中的应用[J].河南大学学报(自然科学版),2014,44(3):347 - 350.
[8] 周文峰,梁晓磊,唐可心,等.具有拓扑时变和搜索扰动的混合粒子群优化算法[J].计算机应用,2020,40(7):1913 - 1918.
[9] SONG H, SULAIMAN M, MOHAMED M.An application of grey wolf optimizer for solving combined economic emission dispatch problems[J].International Review on Modeling and Simulation, 2014,7(5):838 - 844.
[10] GUPTA E, SAXENA A.Robust generation control strategy based on grey wolf optimizer[J].Journal of Electrical Systems, 2015,11(2):174 - 188.
[11] GUHA D, ROY P K, BANERJEE S.Load frequency control of interconnected power system using grey wolf optimization[J].Swarm and Evolutionary Computation, 2016,27:97 - 115.
[12] ZHU A, XU C, LI Z, et al.Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC[J].Journal of Systems Engineering and Electronics, 2015,26(2):317 - 328.
[13] 龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法[J].控制与决策,2017,32(10):1749 - 1757.
[14] 徐松金,龙文.嵌入遗传算子的改进灰狼优化算法[J].兰州理工大学学报,2016,42(4):102 - 108.
[15] 郭振洲,刘然,拱长青,等.基于灰狼算法的改进研究[J].计算机应用研究,2017,34(12):3603 - 3610.
[16] 张悦,孙惠香,魏政磊,等.具有自适应调整策略的混沌灰狼优化算法[J].计算机科学,2017,44(11): 119 - 122.
备注/Memo
收稿日期: 2021-09-26
基金项目: 福建省自然科学基金(2019J01814); 莆田学院校级科研项目(2022033)
作者简介: 陈贞(1977—),女,硕士,副教授,研究方向为智能系统与模式识别、图像处理.