## 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 c} \hline \hline \multirow{2}{*}{**D = 10**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Evaluation Criterion**} \\ \cline{3-7}  & & **Fitness Value** & **PSNR** & **SSIM** & **Iterations** & **Running Time** \\ \hline \multirow{5}{*}{Image1(Forest)} & DE(DE/rand/1) & 931.1964 & 30.9917 & 0.9187 & 165.72 & 5.0252 \\ \cline{2-7}  & DE(DE/best/1) & 927.9442 & 30.466 & 0.9057 & **34.46** & **1.0486** \\ \cline{2-7}  & CSDE & **931.541** & **31.2247** & **0.9242** & 414.88 & 9.6212 \\ \cline{2-7}  & VNCDE & 931.3505 & 31.2011 & 0.9245 & 149.26 & 60.1333 \\ \cline{2-7}  & Our Method & **931.5102** & **31.2395** & **0.9246** & **132.4** & **3.776** \\ \hline \multirow{5}{*}{Image2(Kangaroo)} & DE(DE/rand/1) & 898.2523 & 31.0023 & 0.9084 & 154.12 & 4.7712 \\ \cline{2-7}  & DE(DE/best/1) & 895.4196 & 30.3822 & 0.8963 & **36.68** & **1.1319** \\ \cline{2-7}  & CSDE & **898.481** & **31.1343** & **0.9115** & 430.58 & 9.5338 \\ \cline{2-7}  & VNCDE & 897.977 & 31.0215 & 0.9101 & 201.24 & 79.1909 \\ \cline{2-7}  & Our Method & **898.4808** & **31.1409** & **0.9119** & **125.68** & **3.4951** \\ \hline \multirow{5}{*}{Image3(Actinia)} & DE(DE/rand/1) & 1124.6503 & 30.4593 & 0.799 & 148.24 & 3.9829 \\ \cline{2-7}  & DE(DE/best/1) & 1121.1936 & 29.8745 & 0.8017 & **34.26** & **0.8487** \\ \cline{1-1} \cline{2-7}  & CSDE & **1124.7492** & **30.5089** & **0.8008** & 459.64 & 8.4798 \\ \cline{1-1} \cline{2-7}  & VNCDE & 1124.2897 & 30.4549 & 0.8092 & 338.84 & 125.4491 \\ \cline{1-1} \cline{2-7}  & Our Method & **1124.7372** & **30.5241** & **0.8006** & **140.82** & **3.6164** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results comparison of five algorithms for segmentation of images 1–3 on different criteria when D = 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**D = 8**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Evaluation Criterion**} \\ \cline{3-7}  & & **Fitness Value** & **PSNR** & **SSIM** & **Iterations** & **Running Time** \\ \hline \multirow{5}{*}{Image1(41033)} & DE(DE/rand/1) & 3434.6711 & 29.0786 & 0.7675 & 100.8667 & **2.6089** \\ \cline{2-7}  & DE(DE/best/1) & 3433.6338 & **29.1543** & **0.7683** & **31.2333** & **0.8624** \\ \cline{2-7}  & CSDE & **3434.6965** & 29.0487 & 0.7587 & 366.7333 & 7.6867 \\ \cline{2-7}  & VNCDE & 3434.6609 & 29.0267 & 0.7634 & 120.432 & 65.3453 \\ \cline{2-7}  & Our Method & **3434.6894** & **29.1625** & **0.7686** & **98.8** & 2.7308 \\ \hline \multirow{5}{*}{Image2(385039)} & DE(DE/rand/1) & **4030.9421** & **28.5971** & **0.8249** & **84.4333** & 4.8844 \\ \cline{2-7}  & DE(DE/best/1) & 4030.9409 & 28.5958 & 0.8242 & 88.3333 & **3.3523** \\ \cline{2-7}  & CSDE & 4030.9397 & 28.594 & 0.8233 & 88.1333 & 7.5729 \\ \cline{2-7}  & VNCDE & 4030.9132 & 28.4387 & 0.8078 & 167.9087 & 70.4665 \\ \cline{2-7}  & Our Method & **4030.9418** & **28.5996** & **0.8254** & **84.7667** & **2.3907** \\ \hline \multirow{5}{*}{Image3(145086)} & DE(DE/rand/1) & 3725.232 & 28.5494 & 0.7921 & 94.76 & 4.5831 \\ \cline{2-7}  & DE(DE/best/1) & 3724.4946 & 28.5059 & 0.7916 & **28.54** & **1.5662** \\ \cline{2-7}  & CSDE & **3725.2315** & **28.5514** & **0.7922** & 275.32 & 10.5008 \\ \cline{2-7}  & VNCDE & 3724.9765 & 28.5439 & 0.7786 & 242.579 & 86.7562 \\ \cline{2-7}  & Our Method & **3725.2336** & **28.5533** & **0.7922** & **89.24** & **4.3692** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results comparison of five algorithms for segmentation of images 4–6 on different criteria when D = 8.