## 1 Introduction  
Interior permanent magnet synchronous motors (IPMSMs) are widely used in electric vehicle (EV) traction and industrial servo systems due to their high power density, high speed range and high efficiency [1]. It is still significant to optimize motor average output torque, efficiency while reducing torque fluctuations in order to enhance motor performance, improve motor energy consumption, suppress motor noise and stabilize output power [2]. Traditional multi-objective optimization of IPMSMs relies on large scale finite elements simulations, but the cost of computation increases significantly for a contradiction between high-dimensional design parameters and limited sample capacity [3]. To reduce computational consumption, the surrogate-assisted multi-objective optimization method has wide applications in the field of multi-objective optimization of motors. Ref. [4] proposed a surrogate-based model optimization process for permanent magnet synchronous motors (PMSMs), which uses a comprehensive criterion for model evaluation and selection that takes into account both prediction accuracy and time consumption, and compares the prediction performance of four algorithms, namely Multi-Layer Perceptron (MLP),Generalized Regression Neural Network (GRNN), Support Vector Regression (SVR) and Kriging. In the arithmetic example, the MLP had a better prediction performance for all optimization objectives. Ref. [5] proposed a data-driven-based multi-objective optimization framework of a PMSM with a limited sample size that uses 200 sample points to train an SVR model. Both root mean square error (RMSE) and \(R^{2}\) score achieved the demand of prediction precision. Ref. [6] proposed a fast multi-objective optimization algorithm for electromagnetic components based on an Adaptive Neural Network (Adaptive NN) that updates the weights during the optimization process, which effectively accelerates the optimization computation time without losing the optimization quality. Ref. [7] compared 11 surrogate models based on RMSE during the process of multi-objective optimization of an EV's traction motor. Kriging, Gradient Boosting Decision Tree (GBDT), MLP and Radial Basis Function (RBF) methods were chosen for different optimization objectives to build the surrogate model to minimize the RMSE for each objective, and the optimization results predicted by the surrogate model were very close to the validation results of finite elements. In the process of building surrogate models, a series of experimental design methods are introduced to achieve modeling under finite samples of motors. Under high-dimensional limited samples, the parameters of the model may be biased, thus leading to a decrease in prediction accuracy [8], so it is important to choose a suitable way to fill the experimental space. Among the experimental space-filling designs, Latin hypercube sampling (LHS) is widely used in motor optimization, which seeks to maximize the acquisition of information about the unknown design space while minimizing the number of samples by optimizing the sample point selection strategy [9; 10; 11; 12]. It is obvious that selecting key design parameters that have significant impacts while eliminating unnecessary or redundant parameters could lead to a lower model complexity. There are various approaches to selecting key variables. Here, they are simply grouped into two categories: statistically based analysis methods, which are often described as sensitivity or correlation analysis, and machine learning-based analysis methods, which are usually described as feature selection. Commonly used statistically based analysis methods are Pearson correlation analysis [13; 14], Spearman correlation analysis [15], analysis of variance (ANOVA) [5], signal-to-noise ratio analysis [5; 16], etc. Among the commonly used machine learning-based analysis methods, random forest (RF)-based feature selection methods stand out in motor optimization. Ref.