This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO’s applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.

本文介绍了Ant Lion Optimizer（ALO）的全面概述。ALO是Mirjalili在2015年引入的一种新颖的基于元启发式群的方法，用于模拟自然生活中蚂蚁的狩猎行为。这篇综述着重介绍了利用ALO算法解决各种优化问题的应用程序。在ALO中，确定最佳解决方案是通过查找最小值或最大值来解决某个问题来增强优化过程中功能和效率的性能。由于引入了决策制定，元启发式算法已成为研究的重点，并评估了解决各种优化问题的好处。另外，本文还介绍了ALO变体，例如二进制，修饰，杂交，增强等。ALO应用程序的分类包括基准功能，机器学习应用程序，网络应用程序，工程应用程序，软件工程和图像处理。最后，根据文献中发表的评论文章，ALO算法主要用于解决各种优化问题。提出概述并审查ALO应用程序是此审查文件的主要目的。