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

Author:

SHOAIB MUHAMMAD12ORCID,TABASSUM RAFIA1,NISAR KOTTAKKARAN SOOPPY3ORCID,RAJA MUHAMMAD ASIF ZAHOOR4ORCID,SHAH FAROOQ AHMED1ORCID,ALQAHTANI MOHAMMED S.56ORCID,SALEEL C. AHAMED7ORCID,ALMOHIY H. M.5ORCID

Affiliation:

1. Department of Mathematics, COMSATS University, Islamabad, Attock Campus, Pakistan

2. Yuan Ze University, AI Center, Taoyuan 320, Taiwan

3. Department of Mathematics, College of Sciences and Humanities, Prince Sattam Bin AbdulAziz University, Al Kharj 16278, Saudi Arabia

4. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

5. Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia

6. BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester LE1 7RH, UK

7. Department of Mechanical Engineering, College of Engineering, King Khalid University, Asir-Abha 61421, Saudi Arabia

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 ([Formula: see text] and vector ([Formula: see text]. There are four classes in host population representing susceptible host [Formula: see text], exposed host [Formula: see text], asymptomatic host [Formula: see text] and infectious host [Formula: see text] whereas in vector susceptible [Formula: see text] and infectious [Formula: see text] 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.

Funder

Deanship of Scientific Research at the King Khalid University

Prince Sattam bin AbdulAziz University

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Geometry and Topology,Modeling and Simulation

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