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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 14/2020

31.10.2019 | Review Article

Moth–flame optimization algorithm: variants and applications

verfasst von: Mohammad Shehab, Laith Abualigah, Husam Al Hamad, Hamzeh Alabool, Mohammad Alshinwan, Ahmad M. Khasawneh

Erschienen in: Neural Computing and Applications | Ausgabe 14/2020

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Abstract

This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems, economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms. Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.
Vorheriger Artikel Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions
Nächster Artikel Ramp-based twin support vector clustering

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Metadaten
Titel
Moth–flame optimization algorithm: variants and applications
verfasst von
Mohammad Shehab
Laith Abualigah
Husam Al Hamad
Hamzeh Alabool
Mohammad Alshinwan
Ahmad M. Khasawneh
Publikationsdatum
31.10.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04570-6

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