## 3 Original DE Algorithm
### Experiment Results and Analysis
#### 4.3.2 Contrast Experiments for High-Dimensional Segmentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**D = 9**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Evaluation Criterion**} \\ \cline{3-7}  & & **Fitness Value** & **PSNR** & **SSIM** & **Iterations** & **Running Time** \\ \hline \multirow{5}{*}{Image4(41033)} & DE(DE/rand/1) & 3441.5956 & 29.3233 & 0.7951 & 107.9333 & **2.9114** \\ \cline{2-7}  & DE(DE/best/1) & 3439.5199 & 29.2966 & 0.7889 & **31.1333** & **0.8228** \\ \cline{2-7}  & CSDE & **3441.6141** & **29.3404** & **0.7969** & 333.4 & 6.212 \\ \cline{2-7}  & VNCDE & 3441.543 & 29.2994 & 0.7129 & 141.25 & 62.6528 \\ \cline{2-7}  & Our Method & **3441.5978** & **29.3406** & **0.7959** & **107.6333** & 3.033 \\ \hline \multirow{5}{*}{Image5(385039)} & DE(DE/rand/1) & 4038.7195 & 28.7989 & 0.8417 & **106.2** & 3.0852 \\ \cline{2-7}  & DE(DE/best/1) & 4038.3371 & **28.9104** & **0.8461** & 28.7333 & **0.7629** \\ \cline{2-7}  & CSDE & **4038.7681** & 28.7269 & 0.8376 & 500.7 & 11.3685 \\ \cline{2-7}  & VNCDE & 4038.5453 & 28.6893 & 0.8369 & 153.5 & 69.7843 \\ \cline{2-7}  & Our Method & **4038.7209** & **28.8138** & **0.8424** & **104.2667** & **2.88** \\ \hline \multirow{5}{*}{Image6(145086)} & DE(DE/rand/1) & 3734.052 & 28.6686 & 0.8021 & 109.38 & **4.9802** \\ \cline{2-7}  & DE(DE/best/1) & 3733.1225 & 28.6688 & 0.8035 & **106.2** & **4.8643** \\ \cline{1-1} \cline{2-7}  & CSDE & **3734.1269** & **28.6798** & **0.8041** & 357.32 & 11.8959 \\ \cline{1-1} \cline{2-7}  & VNCDE & 3734.1235 & 28.6701 & 0.7809 & 290.74 & 107.641 \\ \cline{1-1} \cline{2-7}  & Our Method & **3734.1244** & **28.6704** & **0.8039** & **108** & 5.9049 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results comparison of five algorithms for segmentation of images 4–6 on different criteria when D = 9. To further evaluate the performance of the segmentation of our proposed algorithm, we compared the proposed algorithm with the original DE, DE with DE/best/1 operator, CSDE [20], and VNCDE [21] on several evaluation criteria. The results of these algorithms are obtained under different dimensions. It can be seen from Table 1 that our proposed method presents a relatively better performance in terms of computational speed and segmentation effect. A forest image, kangaroo image and actinia image were selected as experimental images to testify the segmentation effect and the computational efficiency of the OTSU algorithm. Taking \"Forest picture\" as an example, the PSNR value of the image is 30.1521, which obviously precedes other comparative algorithms. The SSIM value of the image is 0.8947, which is basically the same as other algorithms, indicating the low image distortion, and the quality of the image after segmentation is guaranteed. On the premise of maintaining the segmentation effect, the running time of our proposed method is 2.8 times shorter than that of the CSDE algorithm, reduced from 7.5639 to 2.6921, which decreased by more than 60% and greatly improved the running speed. Iteration times were 108.32, although this cannot compare with DE using the DE/best/1 strategy but is better than the original DE and CSDE, which reflects the fast convergence of the algorithm to a certain extent. Analysis of the other two images also reached a similar conclusion. Regarding the experiment of the second group of pictures, under the same experimental settings as the first group of pictures, it can also be seen that although the DE algorithm using DE/best/1 has a better performance in terms of time, the accuracy of the algorithm is relatively low. In the case of ensuring high precision, our proposed algorithm has obvious advantages in comprehensive performance. The image after segmentation is shown in Figures 3 and 4.