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;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：12381 / 12401
相关论文
共 50 条
  • [1] Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications
    Abualigah, Laith
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (16): : 12381 - 12401
  • [2] Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications
    Laith Abualigah
    Neural Computing and Applications, 2020, 32 : 12381 - 12401
  • [3] New Variants of the Multi-Verse Optimizer Algorithm Adapting Chaos Theory in Benchmark Optimization
    Amezquita, Lucio
    Castillo, Oscar
    Soria, Jose
    Cortes-Antonio, Prometeo
    SYMMETRY-BASEL, 2023, 15 (07):
  • [4] A novel hybrid multi-verse optimizer with queuing search algorithm
    Wang, Yuan
    Yu, Xiaobing
    Wang, Xuming
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 44 (06) : 9821 - 9845
  • [5] Dragonfly algorithm: a comprehensive survey of its results, variants, and applications
    Mohammad Alshinwan
    Laith Abualigah
    Mohammad Shehab
    Mohamed Abd Elaziz
    Ahmad M. Khasawneh
    Hamzeh Alabool
    Husam Al Hamad
    Multimedia Tools and Applications, 2021, 80 : 14979 - 15016
  • [6] Dragonfly algorithm: a comprehensive survey of its results, variants, and applications
    Alshinwan, Mohammad
    Abualigah, Laith
    Shehab, Mohammad
    Abd Elaziz, Mohamed
    Khasawneh, Ahmad M.
    Alabool, Hamzeh
    Al Hamad, Husam
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (10) : 14979 - 15016
  • [7] AN IMPROVED MULTI-VERSE OPTIMIZER ALGORITHM FOR MULTI-SOURCE ALLOCATION PROBLEM
    Song, Ruixing
    Zeng, Xuewen
    Han, Rui
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2020, 16 (06): : 1845 - 1862
  • [8] A Percentile Multi-Verse Optimizer Algorithm applied to the Knapsack problem.
    Valenzuela, Matias
    Jorquera, Lorena
    Valenzuela, Pamela
    Pinto, Hernan
    Caceres, Camilo
    2019 14TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI), 2019,
  • [9] Multi-Verse Optimizer: a nature-inspired algorithm for global optimization
    Mirjalili, Seyedali
    Mirjalili, Seyed Mohammad
    Hatamlou, Abdolreza
    NEURAL COMPUTING & APPLICATIONS, 2016, 27 (02): : 495 - 513
  • [10] Multi-Verse Optimizer: a nature-inspired algorithm for global optimization
    Seyedali Mirjalili
    Seyed Mohammad Mirjalili
    Abdolreza Hatamlou
    Neural Computing and Applications, 2016, 27 : 495 - 513
12345