# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 4 Experiment
### Comparison of Algorithm Experimental Results and Analysis on Different Test Problems
** & **NSGA-II** & **MOEA/D** & **NSGA-III** & **LMEA** & **DVA-TPCEA** \\ \hline \multirow{8}{*}{ \begin{tabular}{c} WF66 \\ 10 \\ \end{tabular}  } & 100 & 1.3310e+0 & 1.8377e+0 & 1.2214e+0 & 1.1984e+0 & 1.8032e+0 \\  & & (2.00e-2) & (1.50e-1) & (2.17e-4) & (1.23e-2) & (6.65e-2) \\ \cline{2-7}  & 500 & 1.3600e+0 & 1.7878e+0 & 1.2151e+0 & 2.3043e+0 & **1.0884e+0** \\  & & (1.79e-2) & (2.32e-2) & (2.82e-4) & (7.79e-2) & **(1.81e-1)** \\  & & 1000 & 1.3085e+0 & 1.2368e+0 & 1.8459e+0 & 2.1601e+0 & 2.3814e+0 \\  & & (1.01e-1) & (**1.30e-1**) & (2.41e-3) & (1.55e-1) & (9.84e-2) \\ \cline{2-7}  & & 100 & 4.9272e+0 & 1.1097e+1 & 5.9375e+0 & 4.8672e+0 & **4.3412e+0** \\  & & (6.96e-2) & (8.60e-2) & (8.70e-2) & (8.30e-2) & **(2.01e-1)** \\ \cline{2-7}  & 10 & 500 & 6.0393e+0 & 1.1009e+1 & 5.8483e+0 & 8.6245e+0 & 9.4774e+0 \\  & & (2.33e-1) & (8.31e-2) & (1.73e-3) & (5.96e-1) & (3.52e-1) \\ \cline{2-7}  & & 1000 & 3.5612e+0 & 1.3612e+1 & 1.5523e+0 & 7.1621e-1 & 7.6823e-2 \\  & & (5.97e-1) & (1.43e-3) & (7.04e-2) & (4.38e-3) & (1.41e-3) \\ \hline \multirow{8}{*}{ \begin{tabular}{c} WF67 \\ 100 \\ \end{tabular}  } & 100 & 1.4690e+0 & 1.9565e+0 & 1.2262e+0 & 1.1698e+0 & 2.4718e+0 \\  & & (3.03e-2) & (1.81e-1) & (8.17e-4) & (7.04e-3) & (1.28e-1) \\ \cline{2-7}  & 500 & 1.5186e+0 & 2.0551e+0 & 1.2185e+0 & 1.3492e+0 & 1.1392e+0 \\  & & (3.16e-2) & (4.85e-1) & (3.37e-3) & (1.09e-1) & **(2.48e-1)** \\  & 1000 & 1.3766e+0 & 1.9833e+0 & 2.4480e+0 & 1.1412e+0 & 1.3606e+0 \\  & & (7.10e-2) & (9.55e-2) & (4.48e-1) & (4.48e-1) & (2.51e-4) \\ \cline{2-7}  & & 100 & 5.4224e+0 & 1.0814e+1 & 5.8628e+0 & 5.0669e+0 & 5.0064e+0 \\  & & (1.05e-1) & (2.80e-1) & (3.29e-3) & (2.30e-1) & (4.21e-1) \\  & 1000 & 5.0609e+0 & 1.0778e+1 & 5.5889e+0 & 5.3148e+0 & 5.0018e+0 \\  & & (1.46e-1) & (2.54e-1) & (4.43e-1) & (1.29e-4) & (4.72e-1) \\  & & 1000 & 1.6932e+1 & 4.7512e+2 & 7.1156e+0 & 1.1978e-2 & 1.5954e-2 \\  & & (6.00e-2) & (1.27e-4) & (6.29e-2) & (6.03e-4) & (9.10e-3) \\ \hline \multirow{8}{*}{\ \begin{tabular}{c} WF68 \\ 10 \\ \end{tabular}  } & 100 & 1.3654e+0 & 1.9699e+0 & 1.21366e+0 & 1.1856e+0 & 2.4186e+0 \\  & & (2.69e-2) & (1.63e-1) & (7.28e-4) & (1.43e-2) & (1.54e-1) \\ \cline{2-7}  & 500 & 1.3533e+0 & 1.7516e+0 & 1.2151e+0 & 1.1603e+0 & 2.3167e+0 \\  & & (1.78e-2) & (3.94e-2) & (5.08e-4) & (1.99e-4) & (1.48e-1) \\ \cline{2-7}  & 1000 & 1.9746e+0 & 1.9702e+0 & 1.1603e+0 & 2.0962e+0 & 1.1540e+0 \\  & & (4.83e-2) & (2.10e-3) & (1.99e-4) & (4.08e-2) & (1.01e-2) \\ \cline{2-7}  & 100 & 5.2962e+0 & 1.0149e+1 & 5.8582e+0 & 4.8885e+0 & 9.4491e+0 \\  & & (4.39e-2) & (5.24e-1) & (8.79e-3) & (5.57e-2) & (1.21e-1) \\  & 500 & 5.3822e+0 & 9.8035e+0 & 5.8036e+0 & 5.2836e+0 & 1.0120e+1 \\  & & (1.09e+0) & (1.71e+0) & (1.03e-2) & (2.23e-1) & (6.43e-1) \\  & & 1000 & 8.4286e-1 & 7.8715e+2 & 8.8245e+2 & 4.4278e-1 & 8.97866e-2 \\  & & (1.46e-2) & (8.50e-1) & (5.87e-2) & (6.14e-4) & (1.30e-3) \\ \hline \multirow{8}{*}{ \begin{tabular}{c} WF69 \\ 10 \\ \end{tabular}  } & 100 & 1.6060e+0 & 1.6429e+0 & 1.2102e+0 & 1.2224e+0 & 1.2224e+0 & 1.3123e+0 \\  & & (1.47e-1) & (1.25e-1) & (1.24e-3) & (2.12e-2) & (2.23e-1) \\ \cline{1-1}  & 500 & 1.7856e+0 & 1.7641e+0 & 1.2678e+0 & 1.8064e+0 & 1.3228e+0 \\ \cline{1-1}  & & (7.89e-2) & (1.90e-1) & (1.08e-2) & (4.29e-2) & (1.56e-1) \\ \cline{1-1}  & 1000 & 1.6997e+0 & 1.5490e+0 & 1.7641e+0 & 1.6878e+0 & 1.2061e+0 \\ \cline{1-1}  & & (5.75e-2) & (1.27e-1) & (3.33e-1) & (1.04e-1) & (9.50e-3) \\ \cline{1-1} \cline{2-7}  & & 100 & 5.8947e+0 & 9.8124e+0 & 5.8186e+0 & 4.9195e+0 & 5.0013e+0 \\ \cline{1-1}  & & (1.69e-1) & (2.20e-1) & (2.06e-2) & (4.28e-2) & (2.86e-1) \\ \cline{1-1} \cline{2-7}  & 100 & 500 & 3.5442e+0 & 2.0652e+0 & 3.8012e-1 & 7.3956e-1 & 3.6241e-1 \\ \cline{1-1}  & & (7.09e-3) & (2.42e-4) & (2.47e-2) & (4.35e-3) & (7.95e-3) \\ \cline{1-1} \cline{2-7}  
100 to 1000 decision variables.