Adaptive Metaheuristic Optimization of New Dynamic Preisach Hysteresis Modeling

This study presents a method which improves the accuracy of Preisach model that is able to reproduce the magnetic response of ferromagnetic material to change of magnetic fields, especially at higher frequency. The approach consists in extending an existing model and uses mathematical tools like combining a closed-form Everett function for hysteresis modeling with the Monte Carlo integration method to approximate the Preisach function, making calculations faster and more reliable. To find the best settings for the model, two optimization techniques are used: genetic algorithms (GA) and artificial bee colony (ABC). The model is tested by comparing its predictions to real-world experimental data, and it shows excellent accuracy and efficiency. Between the two techniques, GA performs better in terms of precision and reliability, making it a good choice for solving complex problems in modeling magnetic behavior.