ABSTRACT Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different variants to solve various optimisation problems such as binary, real(continuous), constraint, single-objective, multi-objective, and multimodal MFO has been introduced. Many research papers have been presented and summarised. In this review, a general overview of MFO is presented at first. Then, different variants of MFO are described which are classified into three classes: modified, hybridised, and multi-objective. Furthermore, applications of MFO in Engineering, Computer Science, Wireless Sensor Networks, and other fields are discussed. Finally, many possible and future directions are provided.

中文翻译：

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飞蛾火焰优化的全面回顾：变体、混合体和应用

摘要 飞蛾火焰优化算法（Moth-flame Optimization Algorithm，MFO）是 Mirjalili 在 2015 年受自然界飞蛾导航方法的启发而提出的一种新的元启发式优化算法。它因其令人印象深刻的特点而引起了极大的兴趣，主要是：启动阶段不需要推导信息，参数数量少，实现简单，可扩展和灵活。到目前为止，已经引入了解决各种优化问题的不同变体，例如二元、实数（连续）、约束、单目标、多目标和多模态 MFO。发表和总结了许多研究论文。在这篇评论中，首先介绍了 MFO 的一般概述。然后，描述了 MFO 的不同变体，它们分为三类：修改的、混合的和多目标的。此外，讨论了 MFO 在工程、计算机科学、无线传感器网络和其他领域的应用。最后，提供了许多可能的和未来的方向。