## 4 Simulation Results and Analysis  
Parameter optimization is conducted using the genetic optimization algorithm. The operating environment is selected as the Win10 operating system, the processing system is the Intel(R) Xeon(R) CPU E5-2630 v4 with a main frequency of 2.20 GHz, and the memory is 512 G.  
Taking a single triangular corner reflector as an example, the length of the right-angle side is set to 0.6 m, the working frequency is set to 15 GHz, and the electrical size is 30 times the wavelength. The incident electromagnetic wave range is set to \(\theta=0^{\circ}\sim 90^{\circ}\) and \(\varphi=45^{\circ}\). The Method of Moments (MOM), the Multilevel Fast Multipole Algorithm (MLFMA), the Ray Launching-Geometrical Optics (RL-GO) high-frequency asymptotic method, and the CapsNet + Genetic Algorithm (CapsNet + GA) are respectively adopted to solve the Radar Cross Section (RCS) of the target. The calculation results of the four RCS solving methods are shown in Figure 7. The comparison of the Mean Squared Error (MSE) of RCS value for each angle from \(10^{\circ}\) to \(80^{\circ}\) of the calculation accuracy (the calculation result of the MOM algorithm is taken as the standard, and the corresponding MSE is obtained by performing the mean square error calculation between other methods and the MOM algorithm) and the calculation time are shown in Table 3.  
It can be seen that the MLFMA has the smallest mean square deviation compared with the RL-GO and CapsNet + GA algorithms, with a value of merely 0.0193. However, it suffers from the problem of an excessively long calculation time (2142 s) when solving electrically large targets. In this paper, the CapsNet + GA has the shortest calculation time among these common solving methods, which is 34 s. Meanwhile, compared with the RL-GO high-frequency asymptotic method, it has a lower MSE of 437 (0.187). Then, after setting the number of processes to 40, a random initial population of size 500 is created. The individuals generated in the next generation are calculated through the genetic algorithm. The combination of optimization parameters with the smallest F value is regarded as the best individual. The new generation of individuals is produced under crossover and mutation. The crossover probability is set to 0.8, and the mutation \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Optimization Parameter** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** & **Level 6** & **Level 7** & **Level 8** \\ \hline \(a\) & 580–585 & 585–590 & 590–595 & 595–600 & 600–605 & 605–610 & 610–615 & 615–620 \\ \(b\) & 240–255 & 255–270 & 270–285 & 285–300 & 30–315 & 315–330 & 330–345 & 345–360 \\ \(c\) & 20–25 & 25–30 & 30–35 & 35–40 & 40–45 & 45–50 & 50–55 & 55–60 \\ \(d_{1}\) & 0–12.5 & 12.5–25 & 25–37.5 & 37.5–50 & 50–62.5 & 62.5–75 & 75–87.5 & 87.5–100 \\ \(d_{2}\) & 0–2.5 & 2.5–5 & 5–7.5 & 7.5–10 & 10–12.5 & 12.5–15 & 15–17.5 & 17.5–20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The value level of electromagnetic characteristics fitting optimization parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**A Binary** & **Serial** & **Actual Value** & **A Binary** & **Serial** & **Actual Value** \\
**String** & **Number** & & **String** & **Number** & **Actual Value** \\ \hline
000 & 1 & Level 1 & 100 & 5 & Level 5 \\
001 & 2 & Level 2 & 101 & 6 & Level 6 \\
010 & 3 & Level 3 & 110 & 7 & Level 7 \\
011 & 4 & Level 4 & 111 & 8 & Level 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Strings and their corresponding serial numbers and actual values. \(d_{1}=75\) mm, and the relative position distance of the small-angle reflector is \(d_{2}=0\). The results of the specific parameter optimization process are shown in Table 5.  
The optimization parameters crossed at the 45 th and 46 th iterations, so that the optimization result met the requirements at the 47 th iteration, and the iteration stopped after meeting the convergence requirements.