Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications

This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind of the most recent powerful nature-inspired meta-heuristic algorithms, where it has...

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Bibliographic Details
Published in:Neural computing & applications Vol. 32; no. 16; pp. 12381 - 12401
Main Author: Abualigah, Laith
Format: Journal Article
Language:English
Published: London Springer London 01.08.2020
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Engineering education
Heuristic methods
Image Processing and Computer Vision
Machine learning
Optimization
Optimization algorithms
Probability and Statistics in Computer Science
Review Article
Multi-verse optimizer algorithm
Meta-heuristic optimization algorithms
Optimization problems
Nature-inspired algorithms
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind of the most recent powerful nature-inspired meta-heuristic algorithms, where it has been successfully implemented and utilized in several optimization problems in a variety of several fields, which are covered in this context, such as benchmark test functions, machine learning applications, engineering applications, network applications, parameters control, and other applications of MOA. This paper covers all the available publications that have been used MOA in its application, which are published in the literature including the variants of MOA such as binary, modifications, hybridizations, chaotic, and multi-objective. Followed by its applications, the assessment and evaluation, and finally the conclusions, which interested in the current works on the optimization algorithm, recommend potential future research directions.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04839-1