Computational Mathematics and Computer Modeling with Applications (CMCMA)

Numerical investigation of differential biological models via Gaussian RBF collocation method with genetic strategy

https://doi.org/10.52547/CMCMA.1.2.46
Volume 1, Issue 2 - Serial Number 2
December 2022
Pages 46-64
Computational Mathematics and Computer Modeling with Applications (CMCMA)

Document Type : Regular paper

Authors

Fardin Salehi 1
Soleiman Hashemi Shahraki 1
Mohammad Kazem Fallah 2
Mohammad Hemami 3

1 Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

2 ‎Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea

3 Department of Cognitive Modelling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University

Abstract
In this paper, we use radial basis function collocation method for solving the system of differential equations in the area of biology. One of the challenges in RBF method is picking out an optimal value for shape parameter in Radial basis function to achieve the best result of the method because there are not any available analytical approaches for obtaining optimal shape parameter. For this reason, we design a genetic algorithm to detect a close optimal shape parameter. The population convergence figures, the residuals of the equations and the examination of the ASN2R and ARE measures all show the accurate selection of the shape parameter by the proposed genetic algorithm. Then, the experimental results show that this strategy is efficient in the systems of differential models in biology such as HIV and Influenza. Furthermore, we show that using our pseudo-combination formula for crossover in genetic strategy leads to convergence in the nearly best selection of shape parameter.

Keywords

Radial basis function
Genetic algorithm
HIV
Influenza
Shape parameter
  • Receive Date 13 November 2022
  • Revise Date 01 January 2023
  • Accept Date 04 January 2023

Home

Editorial Board

Guide for Authors

Submit Manuscript

Contact Us

Generative AI policies

Copyright Policy

Licensing Policy

Plagiarism Policy

Self-archiving policies for authors

Open Access Statement