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

被引：0
|
作者：
Abualigah, Laith [1 ]
机构：
[1] Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan
来源：
Neural Computing and Applications | 2020年 / 32卷 / 16期
关键词：
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. © 2020; Springer-Verlag London Ltd; part of Springer Nature;
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页码：12381 / 12401
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