A comprehensive review of moth-flame optimisation: variants, hybrids, and applications

被引：64
作者：
Hussien, Abdelazim G. [1 ]
Amin, Mohamed [2 ]
Abd El Aziz, Mohamed [3 ]
机构：
[1] Fayoum Univ, Fac Sci, Al Fayyum, Egypt
[2] Menoufia Univ, Fac Sci, Shibn Al Kawm, Egypt
[3] Zagazig Univ, Fac Sci, Dept Math, Zagazig, Egypt
来源：
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE | 2020年 / 32卷 / 04期
关键词：
Moth-flame optimisation; swarm intelligence; meta-heuristics; optimisation; nature-inspired algorithm; INSPIRED OPTIMIZER; ALGORITHM; MFO; MODEL; SETS;
D O I：
10.1080/0952813X.2020.1737246
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different variants to solve various optimisation problems such as binary, real(continuous), constraint, single-objective, multi-objective, and multimodal MFO has been introduced. Many research papers have been presented and summarised. In this review, a general overview of MFO is presented at first. Then, different variants of MFO are described which are classified into three classes: modified, hybridised, and multi-objective. Furthermore, applications of MFO in Engineering, Computer Science, Wireless Sensor Networks, and other fields are discussed. Finally, many possible and future directions are provided.
引用
收藏
页码：705 / 725
页数：21
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