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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Published in:	Neural computing & applications Vol. 32; no. 14; pp. 9859 - 9884
Main Authors:	Shehab, Mohammad, Abualigah, Laith, Al Hamad, Husam, Alabool, Hamzeh, Alshinwan, Mohammad, Khasawneh, Ahmad M.
Format:	Journal Article
Language:	English
Published:	London Springer London 01.07.2020 Springer Nature B.V
Subjects:	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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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.
AbstractList	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.
Author	Abualigah, Laith Khasawneh, Ahmad M. Shehab, Mohammad Al Hamad, Husam Alshinwan, Mohammad Alabool, Hamzeh
Author_xml	– sequence: 1 givenname: Mohammad orcidid: 0000-0003-0211-3503 surname: Shehab fullname: Shehab, Mohammad email: moh.shehab12@gmail.com organization: Computer Science Department, Aqaba University of Technology – sequence: 2 givenname: Laith surname: Abualigah fullname: Abualigah, Laith organization: Faculty of Computer Sciences and Informatics, Amman Arab University – sequence: 3 givenname: Husam surname: Al Hamad fullname: Al Hamad, Husam organization: Faculty of Computer Sciences and Informatics, Amman Arab University – sequence: 4 givenname: Hamzeh surname: Alabool fullname: Alabool, Hamzeh organization: Department of Information Technology, College of Computing and Informatics, Saudi Electronic University – sequence: 5 givenname: Mohammad surname: Alshinwan fullname: Alshinwan, Mohammad organization: Faculty of Computer Sciences and Informatics, Amman Arab University – sequence: 6 givenname: Ahmad M. surname: Khasawneh fullname: Khasawneh, Ahmad M. organization: Faculty of Computer Sciences and Informatics, Amman Arab University
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Snippet	This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered...
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SubjectTerms	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
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Title	Moth–flame optimization algorithm: variants and applications
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