## 3 Process of Design Optimization
### Results and Analysis of Optimization Based on MOSSA  
The design of pulse correction projectiles requires an optimal solution that changes with variations in target performance. Improvements in one performance indicator often come at the expense of others. Due to the complexity of the influence of design variables on results, the MOSSA algorithm based on non-dominated sorting is used to seek the Pareto optimal solution using both the DK and SK surrogate models. The flight stability of the projectile is handled using the penalty function method, which applies a penalty term on individuals that violate the constraint of the angle of attack amplitude. The optimization process uses a population size of 80 and a maximum number of iterations of 500 to eliminate accidental errors. Each algorithm is independently run 30 times. The Pareto design sets based on the DK and SK models are shown in Figure 13a,b, respectively. The obtained Pareto solutions all fall on the edge of the LHD experimental samples, indicating that all target performance indicators have been improved. It is observed that there is a conflicting relationship between the maximum angle of attack and the lateral correction distance. Specifically, the larger the lateral correction distance, the larger the maximum trajectory angle of attack. In the decision-making process of the scheme, both the demands for correction capability and flight stability should be comprehensively considered. However, the maximum range and lateral correction capability, as discussed at the beginning of the design, are not strictly mutually exclusive or unrelated. Nonetheless, when improving the maximum vertical correction capability as the target function, the situation is different. Therefore, a better solution can be selected from the Pareto optimal solutions under the premise of meeting dynamic flight stability in the decision-making process of pulse correction projectiles. To conduct a specific performance evaluation of the deterministic optimization design based on the DK surrogate model (Deter_DKM) and the robust optimization design based on the SK surrogate model (Robust_SKM), design points with similar performance indicators from the Pareto solution set are selected for comparative analysis. Table 3 presents the values of the Pareto solutions for deterministic optimization and robust optimization. Table 4 provides a detailed comparison of the corresponding Pareto design solutions in terms of performance results obtained from 6 DOF calculations. The mean and variance of the lateral correction distance, maximum range, and maximum angle of attack within the _aero_factor_ change interval are represented by \(\mu_{i},\sigma_{i}(i=c,r,a)\), respectively. Both optimization results have shown a significant improvement in performance at the design points compared to the original configuration. According to Table 4, the deterministic optimization solution of Deter_DKM has increased the mean value of the lateral correction distance by approximately 78.25% and the mean value of maximum range by approximately 8.40% but has increased the mean value of maximum angle of attack by approximately 2.74\({}^{\circ}\); this is within the _aero_factor_ change interval. Similarly, the robust optimization solution based on SK has increased the mean value of the lateral correction distance by approximately 74.27%, the mean value of the maximum range by approximately 7.89%, and the mean value of the maximum angle of attack by approximately 2.13\({}^{\circ}\). The performance of the two optimization solutions is relatively close, and the deterministic optimization solution has a slight advantage in performance indicators. Figure 14a-c present a comparison of the divergence curves for the target performance, and it is evident that the performance divergence characteristics of the robust optimization scheme based on the SK model are superior.