Multi-verse optimizer: Theory, literature review, and application in data clustering

Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, Seyedali Mirjalili

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

64 Citations (Scopus)


Multi-verse optimizer (MVO) is considered one of the recent metaheuristics. MVO algorithm is inspired from the theory of multi-verse in astrophysics. This chapter discusses the theoretical foundation, operations, and main strengths behind this algorithm. Moreover, a detailed literature review is conducted to discuss several variants of the MVO algorithm. In addition, the main applications of MVO are also thoroughly described. The chapter also investigates the application of the MVO algorithm in tackling data clustering tasks. The proposed algorithm is benchmarked by several datasets, qualitatively and quantitatively. The experimental results show that the proposed MVO-based clustering algorithm outperforms several similar algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA) in terms of clustering purity, clustering homogeneity, and clustering completeness.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages19
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • Data clustering
  • Meta-heuristics Multi-verse optimizer
  • MVO
  • Optimization
  • Swarm intelligence


Dive into the research topics of 'Multi-verse optimizer: Theory, literature review, and application in data clustering'. Together they form a unique fingerprint.

Cite this