Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications

By Wei-guo Zhao, Zhenxing Zhang, Liying Wang. Engineering applications of artificial intelligence, 2020

981 citations recorded by the source corpus.

Abstract A new bio-inspired optimization technique, named Manta Ray Foraging Optimization (MRFO) algorithm, is proposed and presented, aiming to providing a novel algorithm that provides an alternate optimization approach for addressing real-world engineering issues. The inspiration of this algorithm is based on intelligent behaviors of manta rays. This work mimics three unique foraging strategies of manta rays, including chain foraging, cyclone foraging, and somersault foraging, to develop an efficient optimization paradigm for solving different optimization problems. The performance of MRFO is evaluated, through comparisons with other state-of-the-art optimizers, on benchmark optimization functions and eight real-world engineering design cases. The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors. In addition, the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision. The MATLAB codes of the MRFO algorithm are available at https://www.mathworks.com/matlabcentral/fileexchange/73130-manta-ray-foraging-optimization-mrfo .

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/4d36064f41673d47422627331b4bd1599193c51b

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In Artificial Intelligence

1. A. El‐Fergany
2. M. A. Elaziz
3. Tong Han
4. Zhuoran Zhang
5. S. I. Selem
6. Andi Tang
7. Bo Han
8. Lei Xie

Citations in the corpus

• Parameters extraction of PEMFC's model using manta rays foraging optimizer (2020, methodology): “The MRFO has successfully been used to solve different benchmark optimization problems and real-world design problems such as tension spring design, pressure vessel design, welded beam design, speed reducer design, rolling element bearing design, multiple disc clutch brake design, and belleville spr”
• Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, methodology): “Some other algorithms of this category include: grey wolf optimizer (GWO) [30], monarch butterfly optimization (MBO) [31], elephant herding optimization (EHO) [32], moth search algorithm (MSA) [33], manta ray foraging optimization (MRFO) [34],earthworm optimization algorithm (EOA) [35], etc.”
• New machine learning method for image-based diagnosis of COVID-19 (2020, background): “Followed [29], the agents forced to find a new position far from xd bestðtÞ by using a random number as reference to them in the search space instead of the best agent.”