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Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering

  • Ibrahim Aljarah
  • Majdi Mafarja
  • Ali Asghar Heidari
  • Hossam Faris
  • Seyedali MirjaliliEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 811)

Abstract

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ibrahim Aljarah
    • 1
  • Majdi Mafarja
    • 2
  • Ali Asghar Heidari
    • 3
  • Hossam Faris
    • 1
  • Seyedali Mirjalili
    • 4
    Email author
  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.Department of Computer Science, Faculty of Engineering and TechnologyBirzeit UniversityBirzeitPalestine
  3. 3.School of Surveying and Geospatial EngineeringUniversity of TehranTehranIran
  4. 4.Institute of Integrated and Intelligent Systems, Griffith University, NathanBrisbaneAustralia

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