为提高永磁辅助同步磁阻电机（permanent magnet-assisted synchronous reluctance motor, PMa-SynRM）的输出性能，提出了基于核极限学习机（kernel extreme learning machine, KELM）和快速非支配排序遗传算法（nondominated sorting genetic algorithm, NSGA-II）相结合的外转子PMa-SynRM多目标优化设计方法。首先，对PMa-SynRM转子磁障进行初步设计并分析PMa-SynRM工作原理。其次，通过综合敏感性分析评估每个设计变量对优化目标的影响，选取主要优化参数。然后，以高输出转矩、高效率和低转矩脉动为优化目标，建立基于KELM的代理模型。最后，采用NSGA-II进行全局寻优，从NSGA-II生成的Pareto前沿中选择最优解，并通过有限元分析进行验证。仿真结果表明：优化后的电机较初始电机平均转矩提高了15.83%，转矩脉动降低了60.27%，且优化后电机效率较初始电机也有所提高，验证了本文优化设计方法的有效性。

In order to improve the output performance of permanent magnet assisted synchronous reluctance motor (PMa-SynRM), a multi-objective optimization design method for external rotor PMa-SynRM based on kernel extreme learning machine(KELM) and fast non-dominated sorting genetic algorithm (NSGA-II) was proposed. Firstly, the preliminary design of the PMa-SynRM rotor magnetic barrier was carried out and the working principle of the PMa-SynRM was analyzed. Secondly, the influence of each design variable on the optimization goal was evaluated through comprehensive sensitivity analysis, and the main optimization parameters were selected. Thirdly, with high output torque, high efficiency and low torque ripple as the optimization goals, a surrogate model based on KELM was established. Finally, NSGA-II was used for global optimization, and the optimal solution was selected from the Pareto frontier generated by NSGA-II, which was verified by finite element analysis. The simulation results show that the average torque of the optimized motor is increased by 15.83%, the torque ripple is reduced by 60.27%, and the efficiency of the optimized motor is also improved compared with the initial motor, which verifies the effectiveness of the optimized design method proposed in this paper.