Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model

This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-...

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Bibliographic Details
Published in:Mathematics and computers in simulation Vol. 212; pp. 234 - 248
Main Authors: Umar, Muhammad, Sabir, Zulqurnain, Raja, Muhammad Asif Zahoor, Baskonus, Haci Mehmet, Ali, Mohamed R., Shah, Nehad Ali
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2023
Subjects:
Artificial neural networks
Hepatitis B virus system
Infection disease
Sequential quadratic algorithm scheme
Statistical analysis
Infection disease
Sequential quadratic algorithm scheme
Statistical analysis
Hepatitis B virus system
Artificial neural networks
ISSN:0378-4754, 1872-7166
Online Access:Get full text
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Summary:This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-SQPS. An error function is designed to use the mathematical form of the hepatitis B virus differential model and its initial conditions. The optimization of the error function is performed by using the hybridization efficiency of the GA-SQPS for the hepatitis B virus infection disease model. For the competence of ANN-GA-SQPS, the matching of obtained and the Adams numerical solutions has been observed. The absolute error is found in good measures for solving a nonlinear hepatitis B virus infection disease model. Moreover, statistical measures using different indices for 50 independent executions and 30 variables have been performed to check constancy, reliability, and effectiveness of the ANN-GA-SQPS.
ISSN:0378-4754
1872-7166
DOI:10.1016/j.matcom.2023.04.034