Abstract:

The Jiles-Atherton(J-A) hysteresis model,characterized by its few parameters and clear physical significance, is widely used in the simulation study of magnetic properties of electromagnetic materials. However,to address the issues of low accuracy and time-consuming parameter identification in the J-A hysteresis model, a hybrid optimization algorithm combining particle swarm optimization(PSO) and ant lion optimization(ALO) is proposed. In the early stage of the algorithm, the global search capability of the PSO algorithm is utilized to rapidly locate the approximate range of the global optimal values for the J-A hysteresis model parameters. Subsequently, as the algorithm enters the deep search stage, the ALO algorithm is introduced. Through the random walk of ants, roulette wheel selection mechanism, and elitist preservation strategy, the algorithm can achieve high-precision convergence in the limited search space, so as to quickly lock the global optimal solution of the model parameters. Simulation and experimental validation demonstrate that this hybrid algorithm exhibits rapid convergence and high accuracy in model parameter identification, and the simulated hysteresis curves are highly consistent with the measured data, validating its practicality and effectiveness.

Key words: particle swarm optimization algorithm, Jiles-Atherton hysteresis model, parameter identification, ant lion optimization algorithm