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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 16/2020

16.03.2020 | Review Article

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

verfasst von: Laith Abualigah

Erschienen in: Neural Computing and Applications | Ausgabe 16/2020

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Abstract

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.
Vorheriger Artikel Evolutionary algorithms and their applications to engineering problems
Nächster Artikel Full-reference image quality metric for blurry images and compressed images using hybrid dictionary learning

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Metadaten
Titel
Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications
verfasst von
Laith Abualigah
Publikationsdatum
16.03.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 16/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04839-1

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