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Research Article | Open Access

Analytical methods in fractional biological population modeling: Unveiling solitary wave solutions

Azzh Saad Alshehry1Safyan Mukhtar2,3( )Ali M. Mahnashi4
Department of Mathematical Sciences, Faculty of Sciences, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia
Department of Basic Sciences, Preparatory Year, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Department of Mathematics and Statistics, College of Science, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Department of Mathematics, Faculty of Science, Jazan University, P.O. Box 2097, Jazan 45142, Kingdom of Saudi Arabia
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Abstract

We examine a biological population model of fractional order (FBPM) in this paper using the Riccati-Bernoulli sub-ODE approach. Many scenarios in computational biology make use of this fundamental fractional model. Of particular note is that our study's FBPM uses fractional derivatives to track changes in the density populations. The study is concerned with the construction of new solitary wave solutions for the FBPM, a system of two nonlinear fractional ordinary differential equations. In this investigation, we use the conformable derivative as the fractional derivative. The Backlund transformation is the foundation of the solution process. We create a variety of families of soliton wave solutions and explain different physical behaviours that are inherent in the problems we explore. In particular, we apply the suggested methods to investigate rational, periodic, and hyperbolic solutions. The solutions found in various classes provide insightful information about the underlying physical mechanisms. To sum up, our current methods are superior instruments for analyzing different families of solutions in fractional-order issues.

CLC number: 26A33, 34A08

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AIMS Mathematics
Pages 15966-15987

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Cite this article:
Alshehry AS, Mukhtar S, Mahnashi AM. Analytical methods in fractional biological population modeling: Unveiling solitary wave solutions. AIMS Mathematics, 2024, 9(6): 15966-15987. https://doi.org/10.3934/math.2024773

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Received: 26 January 2024
Revised: 06 April 2024
Accepted: 15 April 2024
Published: 06 May 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)