DESIGN OF BIO-INSPIRED HEURISTIC TECHNIQUE INTEGRATED WITH SEQUENTIAL QUADRATIC PROGRAMMING FOR NONLINEAR MODEL OF PINE WILT DISEASE

Muhammad Shoaib, Rafia Tabassum (), Kottakkaran Sooppy Nisar, Muhammad Asif Zahoor Raja, Farooq Ahmed Shah (), Mohammed S. Alqahtani, C. Ahamed Saleel () and H. M. Almohiy ()
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Muhammad Shoaib: Department of Mathematics, COMSATS University, Islamabad, Attock Campus, Pakistan†Yuan Ze University, AI Center, Taoyuan 320, Taiwan
Rafia Tabassum: Department of Mathematics, COMSATS University, Islamabad, Attock Campus, Pakistan
Kottakkaran Sooppy Nisar: ��Department of Mathematics, College of Sciences and Humanities, Prince Sattam Bin AbdulAziz University, Al Kharj 16278, Saudi Arabia
Muhammad Asif Zahoor Raja: �Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Farooq Ahmed Shah: Department of Mathematics, COMSATS University, Islamabad, Attock Campus, Pakistan
Mohammed S. Alqahtani: �Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia∥BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester LE1 7RH, UK
C. Ahamed Saleel: *Department of Mechanical Engineering, College of Engineering, King Khalid University, Asir-Abha 61421, Saudi Arabia
H. M. Almohiy: �Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-20

Abstract: This investigation aims to investigate the pine wilt disease model (PWDM) employing hybrid bio-inspired algorithm. The artificial neural networks-based genetic algorithm (ANNs-GA) as global search and sequential quadratic programming (SQP) serve as local search framework. The model consists of two populations, i.e. host (h) and vector (v). There are four classes in host population representing susceptible host (Sh), exposed host (Eh), asymptomatic host (Ah) and infectious host (Ih) whereas in vector susceptible (Sv) and infectious (Iv) class are present. Activation function is introduced for the formulation of the fitness-based function as mean squared error by using nonlinear PWD equations for the accomplishment of ANNs-GASQP paradigm. The stability, robustness and effectiveness of proposed paradigm is comparatively evaluated through Adam numerical scheme with absolute error analysis. Computational complexity of GASQP is determined by convergence criteria of best global weight, fitness evaluation, time, generations, iterations, function counts and mean square error. Moreover, the statistical analysis is performed via Theil’s inequality coefficients (TICs), mean of absolute deviation (MAD) and root mean squared error (RMSE) for multiple trials of ANNs-GASQP. Results reveal that accuracy is obtained up to 3–11 decimal places which proves the reliability of proposed ANNs-GASQP solver.

Keywords: Genetic Algorithms; Artificial Neural Network; Sequential Quadratic Programming; Pine Wilt Disease; Hybridization Procedure (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401485

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