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This research has been cited in:

Article

A Study on Equity and Distribution of National Income and Poverty MethodJournal of Enterprise and Business Intelligence10.53759/5181/JEBI202101023
Article

Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its ApplicationInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.296258

DNA Epidemic Model Construction and Dynamics Optimization

Yinglian Zhou

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)14(3)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805335|DOI: 10.4018/IJCINI.2020070105

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MLA

Zhou, Yinglian. "DNA Epidemic Model Construction and Dynamics Optimization." IJCINI vol.14, no.3 2020: pp.97-117. http://doi.org/10.4018/IJCINI.2020070105

APA

Zhou, Y. (2020). DNA Epidemic Model Construction and Dynamics Optimization. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(3), 97-117. http://doi.org/10.4018/IJCINI.2020070105

Chicago

Zhou, Yinglian. "DNA Epidemic Model Construction and Dynamics Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.3: 97-117. http://doi.org/10.4018/IJCINI.2020070105

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Abstract

In order to solve some complex optimization problems, the SIR-DNA algorithm was constructed based on the DNA-based SIR (susceptible-infectious-recovered) infectious disease model. Since infectious diseases attack a very small part of the individual's genes, the number of variables per treatment is small; thus, the natural dimensionality reduction of the algorithm is achieved. Based on the DNA-SIR infectious disease model, different infections can be distinguished in the pathogenesis of viruses. The mechanisms of disease transmission are described by the SIR model, and these are used to construct operators such as SS, SI, II, IR, RR, and RS, so that individuals can naturally exchange information naturally through disease transmission. The test results show that the algorithm has the characteristics of strong search ability and has a high convergence speed for solving complex optimization problems.