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The Journal of Supercomputing

Erschienen in:

04.10.2021

Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

verfasst von: Alireza Salehan, Arash Deldari

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2022

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Abstract

This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

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Titel: Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic
verfasst von: Alireza Salehan
Arash Deldari
Publikationsdatum: 04.10.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 4/2022
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-04100-z

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