改进的动态灰狼优化算法

张小青; 张玉叶; 明正峰

doi:10.1631/FITEE.2000191

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改进的动态灰狼优化算法

常规文章 | Updated：2022-06-06

- 改进的动态灰狼优化算法
  Enhanced Publication
- Improved dynamic grey wolf optimizer
- 信息与电子工程前沿（英文） 2021年22卷第6期页码：877-890
- Affiliations：
  
  School of Physics and Electronic Engineering, Xianyang Normal University, Xianyang 712000, China
  School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China
- Author bio：
  
  ZHANG Xiaoqing, E-mail: 249140543@qq.com
- Funds：
  
  Project supported by the Scientific Research Plan Projects of Shaanxi Education Department (No. 20JK0972), the Natural Science Basic Research Project of Shaanxi Province (No. 2021JM-517), and the Educational and Teaching Reform Research Project of Xianyang Normal University (No. 2017Y004)
- DOI：10.1631/FITEE.2000191
  中图分类号： TP301
- 纸质出版日期：2021-06，
  
  收稿日期：2020-04-24，
  
  修回日期：2021-05-17，
- Accepted：
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张小青, 张玉叶, 明正峰. 改进的动态灰狼优化算法[J]. 信息与电子工程前沿（英文）, 2021,22(6):877-890.

XIAOQING ZHANG, YUYE ZHANG, ZHENGFENG MING. Improved dynamic grey wolf optimizer. [J]. Frontiers of information technology & electronic engineering, 2021, 22(6): 877-890.
张小青, 张玉叶, 明正峰. 改进的动态灰狼优化算法[J]. 信息与电子工程前沿（英文）, 2021,22(6):877-890. DOI： 10.1631/FITEE.2000191.

XIAOQING ZHANG, YUYE ZHANG, ZHENGFENG MING. Improved dynamic grey wolf optimizer. [J]. Frontiers of information technology & electronic engineering, 2021, 22(6): 877-890. DOI： 10.1631/FITEE.2000191.

摘要

在标准灰狼优化算法（GWO）中，搜索狼必须等到其他搜索狼与3个领导狼之间的比较完成后才能更新其当前位置矢量。正因为有此等待时间，标准GWO被视为静态GWO。为消除这种等待时间，提出两种动态GWO算法：第一种动态灰狼优化算法（DGWO1）和第二种动态灰狼优化算法（DGWO2）。在动态GWO算法中，当前搜索狼不需要等待所有其他搜索狼与领导狼的比较，在完成自身或前一匹搜索狼与领导狼的比较后，即可更新其位置矢量。动态GWO算法及时更新搜索狼的位置，提高了算法迭代收敛速度。以动态GWO算法结构为基础，对其他改进GWO算法也进行了一定的性能测验。实验证明，对同一改进GWO算法，以动态GWO结构为基础时的性能总体上优于以静态GWO结构为基础时的性能。

Abstract

In the standard grey wolf optimizer (GWO)

the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period

the standard GWO is seen as the static GWO. To get rid of this waiting period

two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer (DGWO1) and the second dynamic grey wolf optimizer (DGWO2). In the dynamic GWO algorithms

the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves

and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms

which increases the iterative convergence rate. Based on the structure of the dynamic GWOs

the performance of the other improved GWOs is examined

verifying that for the same improved algorithm

the one based on dynamic GWO has better performance than that based on static GWO in most instances.

关键词

群智能灰狼优化算法动态灰狼优化算法优化实验

Keywords

Swarm intelligenceGrey wolf optimizerDynamic grey wolf optimizerOptimization experiment

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