Salehan Alireza, Deldari Arash
Department of Computer Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.
J Supercomput. 2022;78(4):5712-5743. doi: 10.1007/s11227-021-04100-z. Epub 2021 Oct 4.
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.
本研究引入了一种受新冠病毒大流行启发的新的概率和元启发式优化方法。新冠病毒是一种源自未知动物病毒的感染,已知有三种类型,自2019年末以来,新冠疫情一直在迅速蔓延。基于SIR模型,该病毒可以很容易地从一个人传播给几个人,随着时间的推移引发疫情。考虑到这种病毒的特征和行为,本文提出了一种名为新冠病毒优化(CVO)的优化算法,该算法可行、有效且适用。通过将一组基准函数的结果与其他著名优化算法的结果进行比较,评估了该算法在离散和连续问题上的性能。CVO算法旨在通过求解几个连续数学函数以及三个连续和离散应用问题,找到应用问题的合适解决方案。实验结果表明,所提出的优化方法具有可靠、合理且可接受的性能。