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

Weiguo Zhao, Zhenxing Zhang, Liying Wang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number103300
JournalEngineering Applications of Artificial Intelligence
Volume87
DOIs
StatePublished - Jan 2020

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Keywords

  • Constrained problems
  • Engineering design
  • Global optimization
  • Heuristic algorithm
  • Manta ray foraging optimization
  • Metaheuristic
  • Optimization algorithm

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Manta ray foraging optimization : An effective bio-inspired optimizer for engineering applications. / Zhao, Weiguo; Zhang, Zhenxing; Wang, Liying.

In: Engineering Applications of Artificial Intelligence, Vol. 87, 103300, 01.2020.

Research output: Contribution to journalArticle

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