Eur. Phys. J. Plus (2020) 135: 565
https://doi.org/10.1140/epjp/s13360-020-00557-8

Regular Article

A stochastic computational intelligent solver for numerical treatment of mosquito dispersal model in a heterogeneous environment

¹ Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
² Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, 64002, Douliou, Yunlin, Taiwan, ROC
³ Department Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, 43600, Attock, Pakistan
⁴ Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
⁵ Department of Mathematics, COMSATS University Islamabad, Attock Campus, 43600, Attock, Pakistan

^b rajamaz@yuntect.edu.tw

Received: 3 April 2020
Accepted: 21 June 2020
Published online: 13 July 2020

Abstract

In this paper, the design of stochastic computational intelligent solver is presented for the solution of mathematical model representing the dynamics of mosquito dispersal in a heterogeneous environment by feedforward artificial neural networks (FFANNs) trained with genetic algorithms (GAs) aided with sequential quadratic programming (SQP), i.e., FFANN-GASQP. In the scheme FFANN-GASQP, the formulation of fitness function in mean square error sense with continuous mapping-based differential equation models of FFANNs for the mosquito dispersal system and training of these networks are accomplished by integrated competency of GA and SQP. The exactness, reliability and stability of the designed FFANN-GASQP approach are established through comparative studies with Adams numerical results for both single and multiple runs. Outcomes of statistical assessments are used to validate the accuracy and convergence of the designed FFANN-GASQP scheme.