Grey wolf optimizer (GWO) is one of the most popular metaheuristics, and it has been presented as highly competitive with other comparison methods. However, the basic GWO needs some improvement, such as premature convergence and imbalance between exploitation and exploration. To address these weaknesses, this paper develops a hybrid grey wolf optimizer (HGWO), which combines the Halton sequence, dimension learning-based, crisscross strategy, and Cauchy mutation strategy. Firstly, the Halton sequence is used to enlarge the search scope and improve the diversity of the solutions. Then, the dimension learning-based is used for position update to balance exploitation and exploration. Furthermore, the crisscross strategy is introduced to enhance convergence precision. Finally, the Cauchy mutation strategy is adapted to avoid falling into the local optimum. The effectiveness of HGWO is demonstrated by comparing it with advanced algorithms on the 15 benchmark functions in different dimensions. The results illustrate that HGWO outperforms other advanced algorithms. Moreover, HGWO is used to solve eight real-world engineering problems, and the results demonstrate that HGWO is superior to different advanced algorithms.

灰狼优化器（GWO）是最流行的元启发法之一，与其他比较方法相比，它具有很强的竞争力。然而，基本的GWO还需要一些改进，例如过早收敛以及开发和探索之间的不平衡。针对这些弱点，本文开发了一种混合灰狼优化器（HGWO），它结合了 Halton 序列、基于维度学习、十字交叉策略和柯西变异策略。首先，利用Halton序列扩大搜索范围，提高解的多样性。然后，使用基于维度学习的位置更新来平衡开发和探索。此外，引入交叉策略以提高收敛精度。最后，采用柯西变异策略以避免陷入局部最优。通过在不同维度的 15 个基准函数上与先进算法进行比较，证明了 HGWO 的有效性。结果表明 HGWO 优于其他先进算法。此外，HGWO还被用来解决八个现实世界的工程问题，结果表明HGWO优于不同的先进算法。