In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

在本文中，提出了一种改进的灰狼优化器（I-GWO），用于解决全局优化和工程设计问题。提出该改进措施是为了缓解人口多样性的不足，开发与勘探之间的不平衡以及GWO算法的过早收敛。I-GWO算法得益于一种新的运动策略，即基于维度学习的狩猎（DLH）搜索策略，该策略继承自自然界中狼的个体狩猎行为。DLH使用不同的方法为每只狼构造一个邻域，相邻的信息可以在各狼之间共享。DLH搜索策略中使用的维学习增强了本地搜索和全局搜索之间的平衡，并保持了多样性。在CEC 2018基准套件和四个工程问题上评估了提出的I-GWO算法的性能。在所有实验中，将I-GWO与其他六种最新的元启发法进行了比较。结果也通过Friedman和MAE统计检验进行了分析。实验结果和统计测试表明，与实验中使用的算法相比，I-GWO算法具有很高的竞争力，并且通常更具优势。该算法对工程设计问题的结果表明了该算法的有效性和适用性。实验结果和统计测试表明，与实验中使用的算法相比，I-GWO算法具有很高的竞争力，并且通常更具优势。该算法对工程设计问题的结果表明了该算法的有效性和适用性。实验结果和统计测试表明，与实验中使用的算法相比，I-GWO算法具有很高的竞争力，并且通常更具优势。该算法对工程设计问题的结果表明了该算法的有效性和适用性。