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.
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.
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Xiaoqing ZHANG designed the research and processed the data. Yuye ZHANG drafted the manuscript. Zhengfeng MING helped organize the manuscript. Xiaoqing ZHANG, Yuye ZHANG, and Zhengfeng MING revised and finalized the paper.
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Xiaoqing ZHANG, Yuye ZHANG, and Zhengfeng MING declare that they have no conflict of interest.
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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)
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Zhang, X., Zhang, Y. & Ming, Z. Improved dynamic grey wolf optimizer. Front Inform Technol Electron Eng 22, 877–890 (2021). https://doi.org/10.1631/FITEE.2000191
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DOI: https://doi.org/10.1631/FITEE.2000191