## 4 Proposed Algorithm
### Experimental Results
#### 5.3.2 Discussions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**HV** & **Strategy** & \(\dfrac{-}{\zeta}\) & **NSACOWDRL** & **NSACO** & **NSGA-III** & **NSGA-II** & **MOEA/D** \\ \hline \multirow{8}{*}{Strategy 1} & \multirow{4}{*}{Strategy 1} & Small & 0.551697 & 0.515265 & 0.489368 & 0.436566 & 0.512563 \\  & & 0.000503 & 0.0006 & 0.001642 & 0.000742 & 0.002456 \\  & & 0.530147 & 0.507949 & 0.493525 & 0.383 & 0.417351 \\  & & 0.000205 & 8.74E\(-\)05 & 0.00193 & 0.001007 & 0.00279 \\  & & Large & 0.448719 & 0.427708 & 0.406559 & 0.311904 & 0.301467 \\  & & 0.000595 & 0.000319 & 0.003167 & 0.001017 & 0.001066 \\ \cline{2-7}  & \multirow{4}{*}{Strategy 2} & mean gap & 18.67\% & 16.99\% & 17.62\% & 28.56\% & 41.18\% \\ \cline{2-7}  & & Small & 0.548972 & 0.508876 & 0.469451 & 0.423446 & 0.45982 \\  & & 0.00019059 & 0.00036 & 0.001223 & 0.001704 & 0.001119 \\  & & 0.576724 & 0.558853 & 0.510205 & 0.437301 & 0.458765 \\  & & 6.62792E\(-\)05 & 0.000253 & 0.002138 & 0.001383 & 0.001028 \\  & & Large & 0.4549855 & 0.438979 & 0.430791 & 0.328832 & 0.333339 \\  & & 9.52814E\(-\)05 & 0.000192 & 0.002057 & 0.001357 & 0.000807 \\ \cline{2-7}  & \multirow{4}{*}{Strategy 3} & mean gap & 21.11\% & 21.45\% & 15.57\% & 24.80\% & 27.51\% \\ \cline{2-7}  & & Small & 0.607842 & 0.577377 & 0.531816 & 0.500355 & 0.537066 \\  & & 0.000156 & 0.000275 & 0.001741 & 0.000921 & 0.002507 \\  & & 0.548341 & 0.517545 & 0.481369 & 0.38915 & 0.397993 \\  & & Middle & 0.000235 & 0.000321 & 0.005204 & 0.00121 & 0.001877 \\  & & Large & 0.452389 & 0.43685 & 0.414419 & 0.325447 & 0.29446 \\  & & 0.000199 & 0.000382 & 0.001707 & 0.00078 & 0.001737 \\ \cline{2-7}  & & mean gap & 25.57\% & 24.34\% & 22.07\% & 34.96\% & 45.17\% \\ \hline \multirow{8}{*}{Strategy 1} & \multirow{4}{*}{Strategy 1} & Small & 0.081081 & 0.089546 & 0.114181 & 0.125006 & 0.085965 \\  & & 1.93E\(-\)05 & 2.56E\(-\)05 & 0.000217 & 0.000377 & 0.000176 \\  & & 0.07656 & 0.081223 & 0.12775 & 0.132086 & 0.107114 \\  & & 1.51E\(-\)05 & 2.22E\(-\)05 & 0.000873 & 0.000183 & 0.000203 \\  & & Large & 0.075806 & 0.075908 & 0.166999 & 0.113566 & 0.103795 \\  & & 3.33E\(-\)05 & 1.65E\(-\)05 & 0.002057 & 7.47E\(-\)05 & 0.000376 \\ \cline{2-7}  & \multirow{4}{*}{Strategy 2} & mean gap & 6.51\% & 15.23\% & 31.63\% & 14.02\% & 19.74\% \\ \cline{2-7}  & & Small & 0.070188 & 0.078635 & 0.112759 & 0.120658 & 0.08723 \\  & & 1.14E\(-\)05 & 1.22E\(-\)05 & 0.000214 & 0.000263 & 4.24E\(-\)05 \\  & & 0.067106 & 0.069043 & 0.109341 & 0.110483 & 0.093927 \\  & & 2.46E\(-\)05 & 1.16E\(-\)05 & 0.000552 & 0.000121 & 0.000114 \\  & & Large & 0.064821 & 0.065644 & 0.135298 & 0.106542 & 0.091753 \\  & & 1.53E\(-\)05 & 6.89E\(-\)06 & 0.001603 & 0.000113 & 0.000309 \\ \cline{2-7}  & & mean gap & 7.65\% & 16.52\% & 19.19\% & 11.70\% & 7.13\% \\ \hline \multirow{8}{*}{Strategy 3} & \multirow{4}{*}{Strategy 2} & Small & 0.083499 & 0.085558 & 0.116708 & 0.126069 & 0.093247 \\  & & 2.57E\(-\)05 & 4.2E\(-\)05 & 0.00021 & 0.000104 & 0.000116 \\ \cline{1-1}  & & 0.082514 & 0.085099 & 0.13417 & 0.127182 & 0.110645 \\ \cline{1-1}  & & 5.33E\(-\)05 & 2.18E\(-\)05 & 0.000563 & 0.000119 & 0.000152 \\ \cline{1-1}  & & Large & 0.074674 & 0.079224 & 0.13372 & 0.115114 & 0.113762 \\ \cline{1-1} \cline{2-7}  & & 1.47E\(-\)05 & 8.18E\(-\)06 & 0.000623 & 0.00011 & 0.000174 \\ \cline{1-1} \cline{2-7}  & \multirow{4}{*}{Strategy 4} & mean gap & 10.57\% & 7.40\% & 13.01\% & 9.49\% & 18.03\% \\ \cline{1-1} \cline{2-7}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean and variance metric values of algorithms. By analyzing the \"mean gap\", as the interference coefficient increases, the HV and IGD metrics of NSACOWDRL of Strategy 1 have the smallest degradation, followed by Strategy 2, and the algorithm of Strategy 3 has the most degradation. Compared with other strategies, Strategy 1 can maintain the performance of the algorithm better, which verifies the superiority of Strategy 1.