基于粒子群和蚁狮混合优化算法的Jiles-Atherton磁滞模型参数辨识

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关键词：粒子群优化算法；Jiles-Atherton磁滞模型；参数辨识；蚁狮优化算法中图分类号：TM27 文献标志码：A 文章编号：2095-8188（2026）02-0012-07DOI： 10.16628/j. cnki. 2095-8188.2026.02.002

Abstract：The Jiles-Atherton（J-A） hysteresis model，characterizedby 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 stageof thealgorithm，the global search capabilityof 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 deepsearch stage，theALO algorithm is introduced.Through therandom walk of ants， roulettewheel selection mechanism，and elitist preservation strategy，thealgorithmcanachieve high-precision convergence in the limitedsearch space，soas to quicklylock the global optimal solution of the model parameters. Simulationand experimental validation demonstratethatthis hybridalgorithm exhibitsrapidconvergenceand high accuracy in model parameter identification，and the simulated hysteresis curvesarehighly consistent with the measured data，validatingitspracticalityand effectiveness.

刘磊（1998—），男，硕士研究生，研究方向为电力电子与新能源技术。（剩余8015字）