## 4 Simulation Experiment and Result Analysis  
In order to test the performance of the SC-AEFA, the artificial electric field algorithm (AEFA), artificial electric field algorithm with chaotic mapping (IAEFA), butterfly optimization algorithm (BOA), and grasshopper optimization algorithm (GOA) were used as a control group, and six groups of functions (as shown in Table 1) were used for testing [47; 48; 49; 50]. The software used was MATLAB R2016a, the operating environment was a 64-bit Windows 10 operating system, and the computer hardware parameters were as follows: the processor type was an AMD Ryzen 5 3500U, the on-board RAM was 12.0 GB, and the system was a 64-bit operating system. The above five algorithms used the same population size \(N=50\), dimension \(D=30\), and maximum number of iterations \(Maxiteration=1000\). In order to ensure the validity of the experimental results, each group of functions was run independently 20 times, and the mean value and variance of the running results of each algorithm were compared. The mean value represents the final solution accuracy of the algorithm, and the variance \begin{table}
\begin{tabular}{l c c} \hline \hline
**Function** & **Search Space** & **Theoretical Value** \\ \hline \(f_{1}(x)=\sum\limits_{i=1}^{D}x_{i}^{2}\) & \([-100,\,100]\) & 0 \\ \(f_{2}(x)=\sum\limits_{i=1}^{D}|x_{i}|+\int\limits_{i=1}^{D}|x_{i}|\) & \([-10,\,10]\) & 0 \\ \(f_{3}(x)=\sum\limits_{i=1}^{D}\left(\frac{i}{\
u-1}x_{i}\right)^{2}\) & \([-100,\,100]\) & 0 \\ \(f_{4}(x)=max\left\{|x_{i}|\right\},1\leq i\leq D\) & \([-100,\,100]\) & 0 \\ \(f_{5}(x)=\sum\limits_{i=1}^{D-1}\left[100\left(x_{i+1}-x_{i}^{2}\right)^{2}+ \left(x_{i}-1\right)^{2}\right]\) & \([-30,\,30]\) & 0 \\ \(f_{6}(x)=\sum\limits_{i=1}^{D}(|x_{i}+0.5|)^{2}\) & \([-100,\,100]\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The test function. represents the stability of the algorithm. The running results of the five algorithms on the above six standard test functions are shown in Table 2.  
Table 2 shows the mean and variance of 20 independent runs of the test functions for the GOA, BOA, AEFA, IAEFA, and SC-AEFA. It can be seen from the operation results that the performance and accuracy of the SC-AEFA were significantly stronger than those of the other four algorithms. For the function \(f_{6}\), the SC-AEFA can directly search for the optimal value 0, and the optimization effect can reach 100%. Since the function \(f_{5}\) belongs to the banana function, the optimization result of the SC-AEFA is not ideal, but it is still many orders of magnitude more accurate than that of the other four algorithms. Therefore, the performance of the SC-AEFA has obvious competitive advantages. The convergence trend comparison diagram of the five algorithms is presented below to observe the differences between the five algorithms more intuitively. It can be seen from Figure 4 that the SC-AEFA had obvious advantages in the convergence speed compared with the other algorithms. In addition, as shown in Figure 4b, although the SC-AEFA falls into a local optimum in the early stage of the iteration, as the number of iterations increases, the SC-AEFA escapes this situation. This shows that the SC-AEFA has a strong ability to deviate from the local optimum, that is, it has a strong global search ability and robustness. In Figure 4b,d,e, the BOA and SC-AEFA showed little difference in the convergence speed, but from the analysis of the results, the SC-AEFA had a higher accuracy.