Exploring the finest shortest-path traveling salesman optimization application is a typical NP-hard problem. Similarly the solution of the large-scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures Optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large-scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)-based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29-CEC'17 and 10-CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large-scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers.

中文翻译：

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一种有效的改进的非洲秃鹰优化算法，用于旅行商的维度学习搜索和大规模优化应用

探索最佳最短路径旅行商优化应用是一个典型的 NP-hard 问题。同样，大规模优化应用的解决方案也是摆在科学家面前的一大挑战。首先，非洲秃鹰优化算法 (AVOA) 的开发是为了解决连续应用中表现良好的问题。在过去的几个月中，最近的文献工作中提供了许多 AVOA 的增强策略，并且它已被广泛用于解决大规模工程优化应用。本研究提供了一种新修改的基于维度学习搜索 (DLH) 的 AVOA，称为 DLHAV 算法，以解决高度复杂的连续和离散应用程序。它有助于改善狩猎（或剥削）和搜索（或探索）之间的不平衡，缺乏人群多样性，收敛速度慢，陷入局部最优，以及 AVOA 变体的早期收敛。拟议的策略受益于一种新驱动的方法，称为 DLH 搜索方法，这种方法先天于搜索域中秃鹰的单独开发行为。DLH 探索策略使用一种独特的方法为所有秃鹰建立最佳邻域，其中最近的成员信息可以在秃鹰中提供。DLH 有助于改善全球和本地之间的平衡并维持多样性。为了仔细检查 DLHAV 的性能，DLHAV 方法的解决方案在 29-CEC'17 和 10-CEC'20 上使用熟悉的比较方法和一些其他经典优化方法在许多熟悉的旅行商问题/大规模实例上进行了验证。为了获得公正和严格的比较，采用了标准差、均值等描述性统计，并进行了弗里德曼统计检验。本研究中进行的实验解决方案表明，所提出的算法明显优于其他替代优化器。