## 1 Introduction
In order to reduce the cogging torque and torque pulsation, the researchers in ref. [20] adopted [PERSON]'s method to optimize the magnetic poles of the permanent magnet synchronous motor and establish the parameter optimization matrix, and the optimization results showed that orthogonal matrix optimization not only effectively suppressed cogging torque and torque pulsation, but also improved the output torque. In ref. [21], a multi-objective optimization strategy was proposed, based on finite element analysis and a modified particle swarm optimization algorithm with efficiency, a weak magnetic rate, and price as the optimization objectives. The authors of ref. [22] proposed a heuristic structure optimization method for permanent magnet synchronous motors, using motor efficiency and cogging torque as optimization objectives. Most of the above studies required a large number of sampled data points to optimize the motor, an approach which has strong dependence on finite element analysis and suffers from low optimization efficiency. However, the efficiency of multi-objective optimization for electric machines has been greatly improved with the proposed strategy of response-surface methodology combined with intelligent algorithms. In ref. [23], a multi-objective optimization design method was proposed, based on response-surface methodology and the particle swarm optimization algorithm, to improve the performance of levitation force and levitation force pulsation. The researchers in ref. [24] proposed a multi-objective hierarchical optimization strategy in order to improve the thrust and reduce the thrust pulsation of permanent magnet linear synchronous motors, adopting the CCD method to construct the response-surface model, in combination with the multi-objective particle swarm algorithm to complete the optimization design. In ref. [25], a multi-objective optimization method was proposed, based on a combination of design parameter hierarchy and the response-surface method, using the maximum and minimum ant algorithms to obtain the optimal solution of the response-surface model in order to realize the optimization of cogging torque, torque pulsation, and average torque. The researchers in ref. [26] proposed a hybrid artificial bee colony algorithm, combined with a support vector mechanism for drive motor optimization, compared with the performance of the conventional algorithm in two mathematical test functions to verify its significant performance. In the aforementioned study, the local sensitivity formula was commonly used to approximate the sensitivity value when performing parameter sensitivity analysis, and the sampling points were generally small, resulting in problems such as strong randomness and imprecision in the calculation results and leading to an ultimately suboptimal effect. In this paper, in order to address the problems of low efficiency and poor optimization with the traditional multi-objective optimization strategies for electric motors, we propose an optimization method based on the response-surface method and the improved sparrow algorithm. In contrast to the traditional determination methods for the design parameters of a pump motor, we take into account the cooling demand of the engine during actual operation, thereby effectively avoiding the problem of large motor volume design with excess output capacity. At the same time, we adopted improved Latin hypercubic sampling and the correlation coefficient method to calculate the parameter sensitivity, resulting in more accurate and universal calculations of parameter sensitivity and effectively improving the optimization effect. Moreover, in order to better solve the multi-objective optimization problem for permanent magnet pump motors, we adopted the improved sparrow algorithm to optimize the response-surface model.