Parameters Optimization of the Chemical Reaction Hysteresis Model Using Genetic Algorithms and the Artificial Bee Colony Method

This paper presents the application of both genetic algorithm (GA) and artificial bee colony (ABC) method for parameter identification for the chemical hysteresis model. This model is known to be based on physics approaches, and it is characterized by nine parameters, which describe the reversible and irreversible magnetization mechanisms. Splitting the parameter optimization in two parts using hysteresis curves at various amplitudes offers a more efficient way of solving the optimization problem. Based on the root mean squared error between modeled and experimental B-H loops, it has been shown that GA delivers lower errors in shorter time.