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

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 (S-h), exposed host (E-h), asymptomatic host (Ah) and infectious host (I-h) whereas in vector susceptible (Sv) and infectious (I-v) 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.

Automated summary

This study investigates the pine wilt disease model (PWDM) using a hybrid bio-inspired algorithm. The artificial neural networks-based genetic algorithm (ANNs-GA) is used for global search, and sequential quadratic programming (SQP) serves as the local search framework. The model consists of two populations, host (h) and vector (v), each having different classes. The proposed ANNs-GASQP solver shows stability, robustness, and effectiveness with high accuracy.