RESUMEN

The purpose of this article is to investigate the optimal control of nonlinear fractional order chaotic models of diabetes mellitus, human immunodeficiency virus, migraine and Parkinson's diseases using genetic algorithms and particle swarm optimization. Mathematical chaotic models of nonlinear fractional order type of the above diseases were presented. Then optimal control for each of the models and numerical simulation was done using genetic algorithm and particle swarm optimization algorithm. The results of the genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method is also very successful and the results are very close to the genetic algorithm method. Very low values of MSE and RMSE errors indicate that the simulation is effective and efficient. Also, Lie symmetry was calculated for the proposed models and the results were presented.

Asunto(s)

Diabetes Mellitus , Enfermedad de Parkinson , Humanos , VIH , Algoritmos , Modelos Teóricos , Simulación por Computador

RESUMEN

Recent advances in optimal diabetes control have made it possible for diabetic patients to live longer, healthier, and happier lives. In this research, particle swarm optimization and genetic algorithm are applied in order to control the non-linear fractional order chaotic system of glucose-insulin optimally. A fractional system of differential equations discussed the chaotic behavior of the growth of the blood glucose system. Particle swarm optimization and genetic algorithm were used to solve the presented optimal control problem. The results showed that when the controller is applied from the beginning, the results of the genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method is also very successful and the results are very close to the genetic algorithm method.