Survey of Grey Wolf Optimization Algorithm

doi:10.3778/j.issn.1002-8331.2501-0374

Abstract

Abstract: The grey wolf optimizer (GWO) has attracted significant attention and application across a variety of optimization problems, owing to its rapid convergence speed, straightforward parameter settings, and ease of implementation. To remain current with the latest research findings and encourage further exploration of the GWO, this paper introduces the fundamental principles and mathematical model of the GWO, outlines its implementation steps, and analyzes its time complexity. The paper addresses the shortcomings of the algorithm, such as slow convergence speed and low convergence accuracy, by categorizing and elaborating on various improvement strategies. The paper summarizes the applications of the GWO in diverse fields, including feature selection, scheduling problems, parameter optimization, image segmentation, path planning, and parameter identification. Finally, the paper offers an outlook on future research directions for the GWO.

Key words: metaheuristic algorithm, swarm intelligence optimization algorithm, grey wolf optimizer (GWO), improvement strategy

摘要： 灰狼优化算法凭借快速的收敛速度、简洁的参数设置以及易于实现的特性，在众多优化问题中得到了广泛关注和应用。为了跟踪最新研究成果，促进灰狼优化算法的研究，介绍了灰狼优化算法的基本原理与数学模型，简述了算法的实现步骤，并分析了算法的时间复杂度；针对算法收敛速度慢、收敛精度低等缺点，分类阐述了算法的各种改进策略，同时归纳总结了灰狼优化算法在特征选择、调度问题、参数优化、图像分割、路径规划和参数辨识等领域的应用；对灰狼优化算法未来的研究发展方向进行了展望。

关键词: 元启发式算法, 群体智能优化, 灰狼优化算法（GWO）, 改进策略

JIANG Zhengfeng, LI Chunqing, YANG Xiuzeng, LI Xichun, LIU Xuefei, MO Jie'an, HAN Lingbo. Survey of Grey Wolf Optimization Algorithm[J]. Computer Engineering and Applications, 2025, 61(16): 76-105.

蒋正锋, 李春青, 杨秀增, 李熙春, 柳雪飞, 莫洁安, 韩凌波. 灰狼优化算法研究综述[J]. 计算机工程与应用, 2025, 61(16): 76-105.

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