One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized dragonfly algorithm in applied science are offered in the following area: Machine Learning, Image Processing, Wireless, and Networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than other algorithms in the literature, such as PSO and GA. In general, in this survey the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

中文翻译：

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蜻蜓算法及其在应用科学中的应用——综述

最近开发的启发式优化算法之一是 Mirjalili 的蜻蜓。蜻蜓算法已经展示了其优化不同现实世界问题的能力。它有三个变体。在这项工作中，概述了该算法及其变体。此外，还讨论了该算法的混合版本。此外，在以下领域提供了在应用科学中利用蜻蜓算法的应用结果：机器学习、图像处理、无线和网络。然后将其与其他一些元启发式算法进行比较。此外，该算法在 CEC-C06 2019 基准函数上进行了测试。结果证明该算法具有很强的探索能力，其收敛速度优于文献中的其他算法，如PSO和GA。一般来说，在本次调查中，讨论了算法的优点和缺点。此外，还推荐了一些有助于改进算法弱点的未来工作。本研究旨在为想要研究该算法的研究人员提供有关蜻蜓算法的有益信息。