Open Access

ARTICLE

An Advanced Stochastic Numerical Approach for Host-Vector-Predator Nonlinear Model

Prem Junswang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Soheil Salahshour⁴, Thongchai Botmart^5,*, Wajaree Weera⁵

1 Department of Statistics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand
2 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
3 Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan
4 Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey
5 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand

* Corresponding Author: Thongchai Botmart. Email:

Computers, Materials & Continua 2022, 72(3), 5823-5843. https://doi.org/10.32604/cmc.2022.027629

Received 21 January 2022; Accepted 16 March 2022; Issue published 21 April 2022

Abstract

A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations. The host-vector-predator nonlinear model depends upon five groups or classes, host plant susceptible and infected populations, vectors population of susceptible and infected individuals and the predator population. An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms. For solving the host-vector-predator nonlinear model, a merit function is constructed using the differential model and its associated boundary conditions. The optimization of this merit function is performed using the computational strength of designed integrated heuristics based on interior point method and genetic algorithms. For the comparison, the obtained numerical solutions of networks models optimized with efficacy of global search of genetic algorithm and local search with interior point method have been compared with the Adams numerical solver based results or outcomes. Moreover, the statistical analysis will be performed to check the reliability, robustness, viability, correctness and competency of the designed integrated heuristics of unsupervised networks trained with genetic algorithm aid with interior point algorithm for solving the biological based host-vector-predator nonlinear model for sundry scenarios of paramount interest.

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Keywords

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