一种改进的灰狼优化算法

doi:10.3969/j.issn.0372-2112.2019.01.022

电子学报 ›› 2019, Vol. 47 ›› Issue (1): 169-175.DOI: 10.3969/j.issn.0372-2112.2019.01.022

一种改进的灰狼优化算法

龙文^1,2, 蔡绍洪¹, 焦建军², 伍铁斌³

1. 贵州财经大学贵州省经济系统仿真重点实验室, 贵州贵阳 550025;
2. 贵州财经大学数学与统计学院, 贵州贵阳 550025;
3. 湖南人文科技学院能源与机电工程学院, 湖南娄底 417000

收稿日期:2016-12-23 修回日期:2018-07-15 出版日期:2019-01-25
- 作者简介:
- 龙文男,1977年生于湖南隆回,教授,博士,博士生导师.主要研究方向为智能优化和电力系统建模与控制.E-mail:longwen227@mail.gufe.edu.cn;蔡绍洪 男,1958生于贵州仁怀,教授,博士,博士生导师.主要研究方向为复杂非线性系统建模和非平衡系统控制.E-mail:caish@mail.gufe.edu.cn;焦建军 男,1973年生于湖南邵阳,教授,博士,博士生导师.主要研究方向为复杂系统建模与控制、计算智能.E-mail:jiaojianjun@mail.gufe.edu.cn;伍铁斌 男,1981年生于湖南新化,副教授,博士.主要研究方向为智能优化、工业生产过程建模与控制.E-mail:wutiebin81@csu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61463009）; 贵州省高校科技拔尖人才支持计划 (黔教合KY字.[2017]070）

An Improved Grey Wolf Optimization Algorithm

LONG Wen^1,2, CAI Shao-hong¹, JIAO Jian-jun², WU Tie-bin³

1. Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou 550025, China;
2. School of Mathematics and Statistics, Guizhou University of Finance & Economics, Guiyang, Guizhou 550025, China;
3. School of Energy and Electrical Engineering, Hunan University of Humanities Science & Technology, Loudi, Hunan 417000, China

Received:2016-12-23 Revised:2018-07-15 Online:2019-01-25 Published:2019-01-25

摘要/Abstract

摘要： 灰狼优化算法是最近提出的一种较有竞争力的优化技术.然而，它的位置更新方程存在开发能力强而探索能力弱的缺点.受差分进化和粒子群优化算法的启发，构建一个修改的个体位置更新方程以增强算法的探索能力；受粒子群优化算法的启发，提出一种控制参数a随机动态调整策略.此外，为了提高算法的全局收敛速度，用混沌初始化方法产生初始种群.采用18个高维测试函数进行仿真实验，结果表明：对于绝大多数情形，在相同最大适应度函数评价次数下，本文算法的性能明显优于标准灰狼优化算法.

关键词: 灰狼优化算法, 差分进化, 粒子群优化, 控制参数, 混沌初始化

Abstract: Grey wolf optimization (GWO) algorithm is a relatively novel optimization technique which has been shown to be competitive to other population-based algorithms.However,there is still an insufficiency in canonical GWO regarding its position update equation,which is good at exploitation but poor at exploration.Inspired by differential evolution and particle swarm optimization,the personal best information and the random selected individual from population are used to construct a modified position update equation for enhancing the exploration.Inspired by particle swarm optimization,a random adjustment strategy of control parameterais proposed.In addition,to enhance the global convergence,when producing the initial population,the chaos method is employed.Simulation experiments were conducted on the 18 high-dimensional conventional test functions.The simulation results show that the proposed algorithm provides better performance than basic GWO algorithms in the same or less number of maximum fitness function evaluation in most cases.

Key words: grey wolf optimization algorithm, differential evolution, particle swarm optimization, control parameter, chaotic initialization

中图分类号:

TP301.6

龙文, 蔡绍洪, 焦建军, 伍铁斌. 一种改进的灰狼优化算法[J]. 电子学报, 2019, 47(1): 169-175.

LONG Wen, CAI Shao-hong, JIAO Jian-jun, WU Tie-bin. An Improved Grey Wolf Optimization Algorithm[J]. Acta Electronica Sinica, 2019, 47(1): 169-175.

参考文献

[1] MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[2] LONG W,LIANG X,JIAO J,et al.,An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization[J].Engineering Application of Artificial Intelligence,2018,68:63-80.
[3] 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.
[4] 姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016,31(4):701-708. YAO Peng,WANG Hong-lun.Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer[J].Control and Decision,2016,31(4):701-708.(in Chinese)
[5] 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.
[6] GUPTA E,SAXENA A.Robust generation control strategy based on grey wolf optimizer[J].Journal of Electrical Systems,2015,11(2):174-188.
[7] KOMAKI G,KAYVANFAR V.Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time[J].Journal of Computational Science,2015,8(3):109-120.
[8] ZHU A,XU C,LI Z,et al.Hybridizing grey wolf optimiza-tion 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.
[9] 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. LONG Wen,WU Tiebin.Improved grey wolf optimization algorithm coordinating the ability of exploration and exploitation[J].Control and Decision,2017,32(10):1749-1757.(in Chinese)
[10] 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. XU Song-jin,LONG Wen.Improved grey wolf optimiza-tion embedded with genetic operators[J].Journal of Lanzhou University of Technology,2016,42(4):102-108.(in Chinese)
[11] MIRJALILI S.How effective is the grey wolf optimizer in training multilayer perceptrons[J].Applied Intelligence,2015,42(2):608-619.
[12] 周凌云,丁立新,彭虎,等.一种邻域重心反向学习的粒子群优化算法[J].电子学报,2017,45(11):2815-2824. ZHOU Ling-yun,DING Li-xin,PENG Hu,et al.,Neighborhood centroid opposition-based particle swarm optimization[J].Acta Electronica Sinica,2017,45(11):2815-2824.(in Chinese)
[13] 江善和,王其申,江巨浪.一种新型Skew Tent映射的混沌混合优化算法[J].控制理论与应用,2007,24(2):269-273. JIANG Shan-he,WANG Qi-shen,JIANG Ju-lang.Chaotic hybrid optimization algorithm of a new Skew Tent map[J].Control Theory & Applications,2007,24(2):269-273.(in Chinese)

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一种改进的灰狼优化算法

An Improved Grey Wolf Optimization Algorithm

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