Jiles-Atherton Iron Core Hysteresis Model Parameter Identification with Enhanced Artificial Bee Colony Algorithm

Abstract An enhanced artificial bee colony algorithm is proposed for the problems of insufficient accuracy and slow convergence existing algorithms in J-A model parameter recognition, which introduces Boltzmann selection strategy process nectar sources to dynamically adjust pressure algorithm. To enhance overall optimization performance, at same time, a global factor added search formula capability single bee. Finally, reverse learning used scout quality solution later iteration speed up convergence. The identification comparison experiments are carried out combination with simulation data measured silicon steel sheets. results show that using have better faster speed.