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

21 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

Access to Document

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

Cite this

@inbook{2106fd74056f49f2b34b63a27ca6531d,

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

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

author = "Ibrahim Aljarah and Majdi Mafarja and Heidari, {Ali Asghar} and Hossam Faris and Seyedali Mirjalili",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-12127-3_8",

language = "English",

series = "Studies in Computational Intelligence",

publisher = "Springer Verlag",

pages = "123--141",

booktitle = "Studies in Computational Intelligence",

}

Multi-verse optimizer : Theory, literature review, and application in data clustering. / Aljarah, Ibrahim; Mafarja, Majdi; Heidari, Ali Asghar; Faris, Hossam; Mirjalili, Seyedali.

Studies in Computational Intelligence. Springer Verlag, 2020. p. 123-141 (Studies in Computational Intelligence; Vol. 811).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Multi-verse optimizer

T2 - Theory, literature review, and application in data clustering

AU - Aljarah, Ibrahim

AU - Mafarja, Majdi

AU - Heidari, Ali Asghar

AU - Faris, Hossam

AU - Mirjalili, Seyedali

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Data clustering

KW - Meta-heuristics Multi-verse optimizer

KW - MVO

KW - Optimization

KW - Swarm intelligence

UR - http://www.scopus.com/inward/record.url?scp=85061306836&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-12127-3_8

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

M3 - Chapter

AN - SCOPUS:85061306836

T3 - Studies in Computational Intelligence

SP - 123

EP - 141

BT - Studies in Computational Intelligence

PB - Springer Verlag

ER -

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

Abstract

Publication series

Keywords

Access to Document

Other files and links

Fingerprint

Cite this