## 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 = 8**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Evaluation Criterion**} \\ \cline{3-7}  & & **Fitness Value** & **PSNR** & **SSIM** & **Iterations** & **Running Time** \\ \hline \multirow{4}{*}{Image1(Forest)} & DE(DE/rand/1) & 924.2405 & 30.0992 & 0.8936 & 114.06 & 2.9308 \\ \cline{2-7}  & DE(DE/best/1) & 922.4241 & 29.8563 & 0.8845 & **30.96** & **1.0445** \\ \cline{2-7}  & CSDE & **924.3083** & **30.1454** & **0.8956** & 327.36 & 7.5639 \\ \cline{2-7}  & VNCDE & 924.1787 & 30.1439 & 0.89501 & 130.32 & 45.8256 \\ \cline{2-7}  & Our Method & **924.3082** & **30.1521** & **0.8954** & **99.32** & **2.6921** \\ \hline \multirow{4}{*}{Image2(Kangaroo)} & DE(DE/rand/1) & 889.8179 & 29.8395 & 0.8769 & 107.48 & 2.7631 \\ \cline{2-7}  & DE(DE/best/1) & 887.5622 & 29.6249 & 0.867 & **31.26** & **0.9713** \\ \cline{2-7}  & CSDE & **889.9062** & **29.8681** & **0.8785** & 328.68 & 8.6718 \\ \cline{2-7}  & VNCDE & 889.7553 & 29.8422 & 0.8778 & 152.12 & 53.0893 \\ \cline{2-7}  & Our Method & **889.9043** & **29.8668** & **0.8783** & **95.3** & **2.5922** \\ \hline \multirow{4}{*}{Image3(Actinia)} & DE(DE/rand/1) & 1116.6892 & 29.4944 & 0.7632 & 105.12 & 3.5271 \\ \cline{2-7}  & DE(DE/best/1) & 1113.7184 & 29.2255 & 0.7598 & **31.06** & **0.9773** \\ \cline{1-1} \cline{2-7}  & CSDE & **1116.703** & **29.4996** & **0.7638** & 304.02 & 7.1308 \\ \cline{1-1} \cline{2-7}  & VNCDE & 1116.5651 & 29.4879 & 0.764 & 235.86 & 81.6603 \\ \cline{1-1} \cline{2-7}  & Our Method & **1116.7014** & **29.4959** & **0.7636** & **97.24** & **2.8167** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results comparison of five algorithms for segmentation of images 1–3 on different criteria when D = 8. Figure 2: Histogram distribution. \begin{table}
\begin{tabular}{c 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}{*}{Image1(Forest)} & DE(DE/rand/1) & 928.3622 & 30.5839 & 0.9102 & 135.28 & 4.2656 \\ \cline{2-7}  & DE(DE/best/1) & 925.5307 & 30.1935 & 0.8938 & **33.22** & **1.0158** \\ \cline{2-7}  & CSDE & **928.4816** & **30.6411** & **0.9132** & 369.8 & 8.9023 \\ \cline{2-7}  & VNCDE & 928.1165 & 30.5994 & 0.9129 & 134.14 & 54.7727 \\ \cline{2-7}  & Our Method & **928.477** & **30.6273** & **0.9139** & **119.2** & **3.18** \\ \hline \multirow{5}{*}{Image2(Kangaroo)} & DE(DE/rand/1) & 894.7103 & 30.4694 & 0.8953 & 132.66 & 4.14 \\ \cline{2-7}  & DE(DE/best/1) & 891.3213 & 29.9178 & 0.8813 & **33.66** & **1.1719** \\ \cline{2-7}  & CSDE & **894.8415** & **30.5598** & **0.8972** & 384.76 & 8.3398 \\ \cline{2-7}  & VNCDE & 894.7194 & 30.4893 & 0.8969 & 161.24 & 59.9547 \\ \cline{2-7}  & Our Method & **894.8188** & **30.5214** & **0.8967** & **115.98** & **3.2098** \\ \hline \multirow{5}{*}{Image3(Actinia)} & DE(DE/rand/1) & 1121.1749 & 29.8381 & 0.7793 & 124.36 & 3.7242 \\ \cline{2-7}  & DE(DE/best/1) & 1118.2762 & 29.5798 & **0.782** & **33.36** & **1.0423** \\ \cline{2-7}  & CSDE & **1121.2952** & **29.8734** & 0.7808 & 378.92 & 8.1109 \\ \cline{2-7}  & VNCDE & 1120.91 & 29.8418 & 0.7809 & 290.74 & 107.641 \\ \cline{2-7}  & Our Method & **1121.2713** & **29.8735** & **0.7835** & **116.14** & **3.6367** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results comparison of five algorithms for segmentation of images 1–3 on different criteria when D = 9.