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

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

doi:10.1007/978-3-030-12127-3_8

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 proceeding › Chapter › peer-review

87 Citations (Scopus)

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.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Verlag
Pages	123-141
Number of pages	19
DOIs	https://doi.org/10.1007/978-3-030-12127-3_8
Publication status	Published - 1 Jan 2020
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	811
ISSN (Print)	1860-949X

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-12127-3_8

Cite this

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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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AU - Mafarja, Majdi

AU - Heidari, Ali Asghar

AU - Faris, Hossam

AU - Mirjalili, Seyedali

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

Abstract

Publication series

Keywords

UN SDGs

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