Numerical investigation of differential biological models via Gaussian RBF collocation method with genetic strategy | ||
| Computational Mathematics and Computer Modeling with Applications (CMCMA) | ||
| مقاله 7، دوره 1، شماره 2، اسفند 2022، صفحه 46-64 اصل مقاله (801.86 K) | ||
| نوع مقاله: Regular paper | ||
| شناسه دیجیتال (DOI): 10.52547/CMCMA.1.2.46 | ||
| نویسندگان | ||
| Fardin Salehi1؛ Soleiman Hashemi Shahraki1؛ Mohammad Kazem Fallah2؛ Mohammad Hemami* 3 | ||
| 1Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran | ||
| 2‎Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea | ||
| 3Department of Cognitive Modelling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Radial basis function؛ Genetic algorithm؛ HIV؛ Influenza؛ Shape parameter | ||
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آمار تعداد مشاهده مقاله: 221 تعداد دریافت فایل اصل مقاله: 203 |
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