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

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 base...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence Vol. 87; p. 103300
Main Authors: Zhao, Weiguo, Zhang, Zhenxing, Wang, Liying
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2020
Subjects:
Constrained problems
Engineering design
Global optimization
Heuristic algorithm
Manta ray foraging optimization
Metaheuristic
Optimization algorithm
Engineering design
Global optimization
Metaheuristic
Optimization algorithm
Manta ray foraging optimization
Heuristic algorithm
Constrained problems
ISSN:0952-1976, 1873-6769
Online Access:Get full text
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Summary: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2019.103300