## 2 System-Level Design Optimization
### System-Level Optimization Methods
#### 2.2.1 Single-Level Optimization Method  
The logic behind the single-level optimization method is that both the machine and the controller are optimized simultaneously, and the results are evaluated at the same time. The SLOM framework steps are as follows [23]:  
Steps 1-3: Identical to the general framework described above;  
Step 4: Selection of the optimization method. In this step, an optimization method is chosen that will simultaneously optimize both the machine and the controller;  
Step 5: Implementation of the optimization method and evaluation. The final step involves implementing the optimization algorithm for the selected drive system parameters and evaluating the performance of the drive system. This step is repeated until the optimization goals are met. The aforementioned steps are also illustrated in Figure 3, which outlines the general structure of the methodology. It should be noted that, in the last two steps, the optimization and evaluation of both the machine and the controller are carried out simultaneously. The selection of the optimal design is thus performed holistically for the entire system, rather than independently for each component. The main distinction of this general framework lies in the optimization and evaluation stage, where these processes are executed at the system-level, as explicitly shown in Steps 3 and 4. The primary drawback of this optimization method is the significant computational burden placed on the computer due to the complexity of the problem and the simultaneous optimization of both the machine and controller. This challenge is particularly evident when numerical methods, such as the Finite Element Method (FEM), are employed. Below, relevant studies based on this method are presented. Their analysis reveals that the choice of this approach was primarily driven by its simplicity rather than the reduction in computational time. Figure 3: Single-level optimization framework. The authors of [24] proposed a method that integrates Mamdani fuzzy inference system for drive system modeling with the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) for application in the switched reluctance generator. This new approach, called Fuzzy Inference NSGA-III, performs the simultaneous optimization of both the machine and the controller through the adjustment of geometric parameters of the generator and turn-on angle, \(\theta_{on}\), and the conduction angle, \(\theta_{c}\), of the controller. The NSGA-III was chosen due to its superior ability to handle many-objective optimization problems, and because it naturally facilitates the integration of the designer's fuzzy preferences into the optimization process. It uses a reference-point-based non-dominated sorting approach that not only maintains a well-distributed, diverse Pareto front but also inherently supports the processing of many objectives. By extracting the results, a comparison is made between the outcomes of the proposed method and those of NSGA-II. The results of the former show greater improvement but not significantly. However, the study does not specify the software used for analysis and simulation, which could provide valuable insight into the implementation process. Moreover, the algorithm does not yield a global optimum value but rather a range of values within which the near-optimal value lies. Additionally, the authors could have included wider parameter ranges for the optimization process in order not to limit the algorithm's ability to identify the true near-optimal value. In [25], the authors investigate the design of an SRM drive system for application in vertical transportation using JMAG software for system modeling. They perform optimization using the Genetic Algorithm (GA) on the motor, selecting one of four possible motor models, and on the controller, optimizing both the geometric characteristics of the motor and the turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles of the controller.