Journal of Bionic Engineering ›› 2024, Vol. 21 ›› Issue (2): 953-990.doi: 10.1007/s42235-024-00481-y
Advances in Manta Ray Foraging Optimization: A Comprehensive Survey
Farhad Soleimanian Gharehchopogh1; Shaf Ghafouri2; Mohammad Namazi3; Bahman Arasteh4
- 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
Advances in Manta Ray Foraging Optimization: A Comprehensive Survey
Farhad Soleimanian Gharehchopogh1; Shaf Ghafouri2; Mohammad Namazi3; Bahman Arasteh4
- 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
摘要： This paper comprehensively analyzes the Manta Ray Foraging Optimization (MRFO) algorithm and its integration into diverse academic felds. Introduced in 2020, the MRFO stands as a novel metaheuristic algorithm, drawing inspiration from manta rays’ unique foraging behaviors—specifcally cyclone, chain, and somersault foraging. These biologically inspired strategies allow for efective 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 benefts 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.