###### Abstract
Later, the Kriging model was first applied in engineering by [PERSON] et al. As the Kriging model is known to be suitable for representing heavy nonlinearity, it has been used to solve complicated problems of mechanical engineering [9]. [PERSON] et al. recommended the Kriging model as an optimal model for solving nonlinear problems with less than 50 variations [10]. In [11], the optimum design of a flux-switching PM machine was developed using the Kriging model. The cogging torque was minimized by changing the rotor pole arc and tooth width, with constraints on the average torque, torque ripple, and slot fill factor. In [12], the design and optimization of a novel wound field synchronous machine was presented. The rotor shape was optimized to improve torque and reduce torque ripple using the Kriging model and a genetic algorithm (GA). In [13], a study of a novel stator modular ring drive thruster motor was carried out. Multi-objective optimal design was performed combining FEA, a Kriging model, and non-dominated sorting GA II. In [14], an unequal air-gap model for a PMSM was proposed and it was optimized using the Kriging model and a GA. The torque ripple was reduced by 7.7% and the demagnetization temperature was increased. In the previous studies [11; 12; 13; 14], metamodels were generated using only one Kriging model. However, because there are different suitable metamodeling methods depending on the design problem, it is necessary to apply various methods. The use of artificial intelligence technology based on big data has been rapidly spreading and these techniques have been applied to the field of optimal design. Machine learning, a key area of artificial intelligence, is a set of learning program techniques that allow machines and computers to think and learn like humans. This process consists of accumulating data, learning rules, and predicting new data. Deep learning is a field of machine learning that utilizes multi-layered artificial neural networks. \"Deep\" means that the neural network has many layers. Recently, owing to the improvement of computer performance and availability of sufficient digital data, deep learning has become a rapidly developing technology. Deep learning techniques are beginning to be applied in the field of electric machines, but they are still limited to the field of fault diagnosis only [15; 16; 17]. In [15], a diagnosis algorithm for PMSMs was studied using a convolution neural network (CNN). The system, using a one-dimensional (1D)-CNN, showed 98.8% accuracy in classifying five failure modes of the PMSM. In [16], a novel motor fault diagnosis method using deep transfer learning was proposed. The CNN has been adopted as the basic framework for extracting multilevel functions from raw vibration signals. A novel deep learning framework was developed to get an accurate motor fault diagnosis using transfer learning. The test accuracy was nearly 100% for the induction motor dataset [17]. This study differs from previous ones for the following reasons. Firstly, metamodeling using a multi-layer perceptron (MLP), which is a kind of deep learning, was carried out and used for shape optimization of a PMSM. As mentioned, the use of deep learning techniques is still confined to motor fault diagnosis. Secondly, the optimal design results obtained using the MLP and the results of applying the Kriging model, which has been widely used, are compared. In the past, there have been many studies on metamodeling conducted by applying a single method, but because there is a suitable metamodel for each optimal design problem, it is necessary to create and compare metamodels in various ways. Finally, analysis was performed while changing the weighting of the multi-objective function. In the case of an optimal design with a multi-objective function, the optimal design results depend on the weighting of the multi-objective function. Therefore, this study examines the effects on the optimal design result of adjusting the weighting ratio of the multi-objective function.