An Efficient Computational Approach for a Fractional-Order Biological Population Model With Carrying Capacity
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Pergamon-elsevier Science Ltd
Open Access Color
Green Open Access
No
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Publicly Funded
No
Abstract
In this article, we examine a fractional-order biological population model with carrying capacity. The blended homotopy techniques pertaining to the Sumudu transform are utilized to explore the solutions of a nonlinear fractional-order population model with carrying capacity. The fractional derivative of the Caputo type is utilized in the proposed investigation. The numerical computations indicate the sufficiency of the iterations for the improved estimations of the solutions of this fractional-order biological population model which exemplifies the potency and soundness of the utilized schemes. The analysis explored through the utilization of the projected methods reveals that both of the schemes are in a great agreement with each other. The variations of the prey and predator populations with respect to time and fractional order of the Caputo derivative are presented and graphically illustrated. (c) 2020 Elsevier Ltd. All rights reserved.
Description
Srivastava, Hari M./0000-0002-9277-8092; Kumar, Devendra/0000-0003-4249-6326
Keywords
Fractional-Order Biological Population Model, Carrying Capacity, Caputo Fractional Derivative, Homotopy Methods, Sumudu Transform, Population dynamics (general), Caputo fractional derivative, Fractional derivatives and integrals, carrying capacity, fractional-order biological population model, sumudu transform, homotopy methods
Fields of Science
0103 physical sciences, 01 natural sciences
Citation
Srivastava, H. M...et al. (2020). "An efficient computational approach for a fractional-order biological population model with carrying capacity", Chaos Solitons & Fractals, Vol. 138.
WoS Q
Q1
Scopus Q
Q1

OpenCitations Citation Count
106
Source
Chaos, Solitons & Fractals
Volume
138
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CrossRef : 111
Scopus : 110
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Mendeley Readers : 20
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116
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Web of Science™ Citations
101
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2
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