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

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Nature-Inspired Optimizers

Part of the book series: Studies in Computational Intelligence ((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.

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Notes

  1. 1.

    http://www.alimirjalili.com/MVO.html.

  2. 2.

    https://github.com/7ossam81/EvoloPy.

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Author information

Authors and Affiliations

  1. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah & Hossam Faris

  2. Department of Computer Science, Faculty of Engineering and Technology, Birzeit University, PoBox 14, Birzeit, Palestine

    Majdi Mafarja

  3. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  4. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

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  1. Ibrahim Aljarah
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  2. Majdi Mafarja
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  3. Ali Asghar Heidari
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  4. Hossam Faris
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  5. Seyedali Mirjalili
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Correspondence to Seyedali Mirjalili .

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  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Aljarah, I., Mafarja, M., Heidari, A.A., Faris, H., Mirjalili, S. (2020). Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_8

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