Skip to main content
Springer Nature Link
Log in

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

  • Review Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Abualigah L, Diabat A (2020) A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Comput Appl 1–21

  2. Bolaji AL, Al-Betar MA, Awadallah MA, Khader AT, Abualigah LM (2016) A comprehensive review: Krill herd algorithm (kh) and its applications. Appl Soft Comput 49:437–446

    Google Scholar 

  3. Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2019) Moth-flame optimization algorithm: variants and applications. Neural Comput Appl 10:1–26

    Google Scholar 

  4. Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 10:1–21

    Google Scholar 

  5. Matyas J (1965) Random optimization. Autom Remote Control 26:246–253

    MathSciNet  MATH  Google Scholar 

  6. Glover F (1989) Tabu search–part I. ORSA J Comput 1:190–206

    MATH  Google Scholar 

  7. Abualigah LM, Khader AT, Hanandeh ES (2018) A novel weighting scheme applied to improve the text document clustering techniques. In: Innovative computing, optimization and its applications, Springer, 2018, pp 305–320

  8. Abualigah LM, Sawaie AM, Khader AT, Rashaideh H, Al-Betar MA, Shehab M (2017) \(\beta\)-hill climbing technique for the text document clustering. New Trends Inf Technol 60:1–10

    Google Scholar 

  9. Koza JR (1992) Evolution of subsumption using genetic programming. In: Proceedings of the first European conference on artificial life, pp 110–119

  10. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11:5508–5518

    Google Scholar 

  11. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    MathSciNet  MATH  Google Scholar 

  12. Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. arXiv:1003.1409

  13. Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), IEEE, vol 2, pp 1470–1477

  14. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19

    Google Scholar 

  15. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68

    Google Scholar 

  16. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513

    Google Scholar 

  17. Abualigah LM, Khader AT, Al-Betar MA, Awadallah MA (2016) A krill herd algorithm for efficient text documents clustering. In: 2016 IEEE symposium on computer applications & industrial electronics (ISCAIE), IEEE, 2016, pp 67–72

  18. Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, Springer, pp 240–249

  19. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical Report, Technical report-tr06, Erciyes university, engineering faculty, computer

  20. Bayraktar Z, Komurcu M, Werner DH (2010) Wind driven optimization (wdo): A novel nature-inspired optimization algorithm and its application to electromagnetics. In: 2010 IEEE antennas and propagation society international symposium, IEEE, 2010, pp 1–4

  21. Hosseini HS, (2007) Problem solving by intelligent water drops. In: 2007 IEEE congress on evolutionary computation, IEEE, 2007, pp 3226–3231

  22. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science, IEEE, pp 39–43

  23. Niu B, Wang H (2012) Bacterial colony optimization. Discrete Dyn Nat Soc. https://doi.org/10.1155/2012/698057

    Article  MathSciNet  MATH  Google Scholar 

  24. Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12:702–713

    Google Scholar 

  25. Abdel-Basset M, Manogaran G, El-Shahat D, Mirjalili S (2018) A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Gener Comput Syst 85:129–145

    Google Scholar 

  26. Garg H (2016) A hybrid pso-ga algorithm for constrained optimization problems. Appl Math Comput 274:292–305

    MathSciNet  MATH  Google Scholar 

  27. Javaid N, Javaid S, Abdul W, Ahmed I, Almogren A, Alamri A, Niaz I (2017) A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10:319

    Google Scholar 

  28. Aydilek IB (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66:232–249

    Google Scholar 

  29. Khoury J, Ovrut BA, Seiberg N, Steinhardt PJ, Turok N (2002) From big crunch to big bang. Phys Rev D 65:086007

    Google Scholar 

  30. Valenzuela M, Peña A, Lopez L, Pinto H (2017) A binary multi-verse optimizer algorithm applied to the set covering problem. In: 2017 4th international conference on systems and informatics (ICSAI), IEEE, 2017, pp 513–518

  31. Gunardi H (2018) Penerapan multi-verse optimizer untuk menyelesaikan asymmetric travelling salesman problem

  32. Abdel-Basset M, El-Shahat D, Faris H, Mirjalili S (2019) A binary multi-verse optimizer for 0–1 multidimensional knapsack problems with application in interactive multimedia systems. Comput Ind Eng 132:187–206

    Google Scholar 

  33. Ewees AA, El Aziz MA, Hassanien AE (2017) Chaotic multi-verse optimizer-based feature selection. Neural Comput Appl 10:1–16

    Google Scholar 

  34. Liu G, Zhang B, Ma X, Wang J (2018) Network intrusion detection based on chaotic multi-verse optimizer. In: Proceedings of the 11th EAI international conference on mobile multimedia communications, ICST (Institute for Computer Sciences, Social-Informatics, 2018, pp 218–227

  35. Bentouati B, Chettih S, Jangir P, Trivedi IN (2016) A solution to the optimal power flow using multi-verse optimizer. J Electr Syst 12:716–733

    Google Scholar 

  36. Pei Y, Zhao S, Yang X, Cao J, Gong Y (2018) Design optimization of a srm motor by a nature-inspired algorithm: multi-verse optimizer. In: 2018 13th IEEE conference on industrial electronics and applications (ICIEA), IEEE, 2018, pp 1870–1875

  37. Zhao H, Han X, Guo S (2018) Dgm (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting. Neural Comput Appl 30:1811–1825

    Google Scholar 

  38. Faris H, Hassonah MA, Ala’M A-Z, Mirjalili S, Aljarah I (2018) A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Comput Appl 30:2355–2369

    Google Scholar 

  39. Faris H, Aljarah I, Mirjalili S (2016) Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl Intell 45:322–332

    Google Scholar 

  40. Shukri S, Faris H, Aljarah I, Mirjalili S, Abraham A (2018) Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Eng Appl Artif Intell 72:54–66

    Google Scholar 

  41. Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2020) Multi-verse optimizer: theory, literature review, and application in data clustering. In: Nature-inspired optimizers, Springer, 2020, pp 123–141

  42. Hu C, Li Z, Zhou T, Zhu A, Xu C (2016) A multi-verse optimizer with levy flights for numerical optimization and its application in test scheduling for network-on-chip. PloS ONE 11:e0167341

    Google Scholar 

  43. Ying N, Chusu R, Yangfeng Z (2016) Based on multi-verse optimizer algorithm for SVM parameter optimization. J Liaoning Tech Univ 12:23

    Google Scholar 

  44. DIF N, ELBERRICHI Z (2017) Microarray data feature selection and classification using an enhanced multi-verse optimizer and support vector machine. In: 3rd international conference on networking and advanced systems

  45. Liu J, He D, (2018) An mutational multi-verse optimizer with Lévy flight. In: international conference on intelligent computing, Springer, pp 841–853

  46. Vivek K, Deepak M, Mohit J, Asha R, Vijander S et al. (2018) Development of multi-verse optimizer (mvo) for labview. In: Intelligent communication, control and devices, Springer, pp 731–739

  47. Abdel-Basset M, Shawky LA, Eldrandaly K (2018) Grid quorum-based spatial coverage for IOT smart agriculture monitoring using enhanced multi-verse optimizer. Neural Comput Appl 2:1–18

    Google Scholar 

  48. Jangir P, Parmar SA, Trivedi IN, Bhesdadiya R (2017) A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Int J Eng Sci Technol 20:570–586

    Google Scholar 

  49. Sayed GI, Darwish A, Hassanien AE (2018) A new chaotic multi-verse optimization algorithm for solving engineering optimization problems. J Exp Theor Artif Intell 30:293–317

    Google Scholar 

  50. Elaziz MA, Oliva D, Ewees AA, Xiong S (2019) Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer. Expert Syst Appl 125:112–129

    Google Scholar 

  51. Trivedi IN, Jangir P, Jangir N, Parmar SA, Bhoye M, Kumar A (2016) Voltage stability enhancement and voltage deviation minimization using multi-verse optimizer algorithm. In: 2016 international conference on circuit, power and computing technologies (ICCPCT), IEEE, pp 1–5

  52. Hassanin MF, Shoeb AM, Hassanien AE (2017) Designing multilayer feedforward neural networks using multi-verse optimizer. In: Handbook of research on machine learning innovations and trends, IGI Global, pp 1076–1093

  53. Liu Y, He Y, Cui W (2018) An improved svm classifier based on multi-verse optimizer for fault diagnosis of autopilot. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC), IEEE, 2018, pp 941–944

  54. Kolluru S, Inamdar A et al (2018) Inherent optical properties retrieval from deep waters using multi verse optimizer. In: Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, International Society for Optics and Photonics, 2018, vol 10784, p 107840F

  55. Dif N, Elberrichi Z (2018) A multi-verse optimizer approach for instance selection and optimizing 1-NN algorithm. Int J Strateg Inf Technol Appl 9:35–49

    Google Scholar 

  56. Sulaiman MH, Mohamed MR, Mustaffa Z, Aliman O (2016) An application of multi-verse optimizer for optimal reactive power dispatch problems. Int J Simul Syst Sci Technol 17:41

    Google Scholar 

  57. Wang X, Luo D, Zhao X, Sun Z (2018) Estimates of energy consumption in china using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation. Energy 152:539–548

    Google Scholar 

  58. Shaheen AM, El-Sehiemy RA (2019) Application of multi-verse optimizer for transmission network expansion planning in power systems. In: 2019 international conference on innovative trends in computer engineering (ITCE), IEEE, 2019, pp 371–376

  59. Fathy A, Rezk H (2018) Multi-verse optimizer for identifying the optimal parameters of PEMFC model. Energy 143:634–644

    Google Scholar 

  60. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73:4773–4795

    Google Scholar 

  61. Abualigah LM, Khader AT, Hanandeh ES (2018a) A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering. Intell Decis Technol 12:3–14

    Google Scholar 

  62. Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125

    Google Scholar 

  63. Tabrizchi H, Javidi MM, Amirzadeh V (2019) Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation. Evol Syst 10:1–13

    Google Scholar 

  64. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Google Scholar 

  65. Malhotra R, Khanna M, Raje RR (2017) On the application of search-based techniques for software engineering predictive modeling: a systematic review and future directions. Swarm Evol Comput 32:85–109

    Google Scholar 

  66. Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Google Scholar 

  67. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

    Google Scholar 

  68. Shehab M, Daoud MS, AlMimi HM, Abualigah LM, Khader AT (2019) Hybridising cuckoo search algorithm for extracting the ODF maxima in spherical harmonic representation. Int J Bio Inspired Comput 14:190–199

    Google Scholar 

  69. Rakshit P, Konar A, Das S (2017) Noisy evolutionary optimization algorithms-a comprehensive survey. Swarm Evol Comput 33:18–45

    Google Scholar 

  70. Gotmare A, Bhattacharjee SS, Patidar R, George NV (2017) Swarm and evolutionary computing algorithms for system identification and filter design: a comprehensive review. Swarm Evol Comput 32:68–84

    Google Scholar 

  71. Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Google Scholar 

  72. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Google Scholar 

  73. Yang X-S (2010) A new metaheuristic bat-inspired algorithm, in: Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, 2010, pp 65–74

  74. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

Authors
  1. Laith Abualigah
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Laith Abualigah.

Ethics declarations

Conflict of Interest

The author declares that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abualigah, L. Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput & Applic 32, 12381–12401 (2020). https://doi.org/10.1007/s00521-020-04839-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04839-1

Keywords