Moth–flame optimization algorithm: variants and applications

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 pow...

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Vydané v:Neural computing & applications Ročník 32; číslo 14; s. 9859 - 9884
Hlavní autori: Shehab, Mohammad, Abualigah, Laith, Al Hamad, Husam, Alabool, Hamzeh, Alshinwan, Mohammad, Khasawneh, Ahmad M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.07.2020
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data mining
Data Mining and Knowledge Discovery
Design engineering
Heuristic methods
Image processing
Image Processing and Computer Vision
Optimization
Power dispatch
Probability and Statistics in Computer Science
Review Article
Moth–flame optimization
Variants of MFO
Optimization problems
Metaheuristic algorithms
ISSN:0941-0643, 1433-3058
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04570-6