This paper comprehensively analyzes the Manta Ray Foraging Optimization (MRFO) algorithm and its integration into diverse academic fields. Introduced in 2020, the MRFO stands as a novel metaheuristic algorithm, drawing inspiration from manta rays’ unique foraging behaviors—specifically cyclone, chain, and somersault foraging. These biologically inspired strategies allow for effective solutions to intricate physical challenges. With its potent exploitation and exploration capabilities, MRFO has emerged as a promising solution for complex optimization problems. Its utility and benefits have found traction in numerous academic sectors. Since its inception in 2020, a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE, Wiley, Elsevier, Springer, MDPI, Hindawi, and Taylor & Francis, as well as at international conference proceedings. This paper consolidates the available literature on MRFO applications, covering various adaptations like hybridized, improved, and other MRFO variants, alongside optimization challenges. Research trends indicate that 12%, 31%, 8%, and 49% of MRFO studies are distributed across these four categories respectively.

本文全面分析了蝠鲼觅食优化（MRFO）算法及其与不同学术领域的融合。MRFO 于 2020 年推出，是一种新颖的元启发式算法，其灵感来自蝠鲼独特的觅食行为，特别是旋风、链式和筋斗觅食。这些受生物学启发的策略可以有效解决复杂的身体挑战。凭借其强大的开​​发和探索能力，MRFO 已成为复杂优化问题的一种有前途的解决方案。它的实用性和好处受到了许多学术领域的关注。自 2020 年成立以来，大量基于 MRFO 的研究已在 IEEE、Wiley、Elsevier、Springer、MDPI、Hindawi 和 Taylor & Francis 等知名国际期刊以及国际会议论文集上发表。本文整合了有关 MRFO 应用的现有文献，涵盖各种适应方案，例如混合型、改进型和其他 MRFO 变体，以及优化挑战。研究趋势表明，12%、31%、8% 和 49% 的 MRFO 研究分别分布在这四个类别中。