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

• 学术论文 • 上一篇    下一篇

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. 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 发布日期: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 Wen1,2, CAI Shao-hong1, JIAO Jian-jun2, WU Tie-bin3   

  1. 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: 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

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