引文信息：
Farhad Soleimanian Gharehchopogh, Shafi Ghafouri, Mohammad Namazi & Bahman Arasteh.Advances in Manta Ray Foraging Optimization: A Comprehensive Survey. Journal of Bionic Engineering，2024，21（2），953- 990.Advances in Manta Ray Foraging Optimization: A Comprehensive Survey
1 Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
2 Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
3 Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran.
4 Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey.
Abstract
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.
Fig. W1 A A foraging manta ray, and B structure of a manta ray.
Fig. W2 MRFOs’ somersault foraging techniques.
Fig. W3 Separation of papers of MRFO based on year and publisher.
Fig. W4 Shows the most important OBL targets for the MRFO.
全文链接：https://rdcu.be/dCMfc
