## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.3 Dynamic Range Adjustment
Its broader search range provides increased flexibility in adjusting solutions, facilitating the exploration of a more comprehensive array of solutions. In Figures 11 and 12, we analyze the convergence curves of MOBA-CV-SARSA-DA and MOBA-CV-SARSA-MA, comparing their performance using various indicators. Regarding hypervolume, MOBA-CV-SARSA-DA not only achieves a faster convergence rate than MOBA-CV-SARSA-MA, thanks to its switching mechanism, but also displays superior overall performance. Both methods demonstrate substantial solution diversity in the early iterations when considering the diversity metric. Table 16 illustrates the results of MOBA-CV derived from \(\alpha\), \(\gamma\) hyperparameter experiments, and MOBA-CV-SARSA with fixed range using reinforcement learning, both surpassing the MOBA-CV proposed by [PERSON] et al. [15] based on optimal intervals of \(\alpha\) and \(\gamma\). In Table 17, both methods successfully identify non-dominated solutions across 30 experiments. Notably, MOBA-CV-SARSA-DA consistently maintains a higher count of non-dominated solutions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Hypervolume**} \\ \cline{2-5}  & **Average** & **Percentage** & **Standard Deviation** \\ \hline \multicolumn{5}{c}{The total number of iterations is 100 times.} \\ \hline \multirow{3}{*}{MOBA-CV} & the best \(\alpha\), \(\gamma\) & 10,967.27 & 0.58\% & 190.33 \\ \cline{2-5}  & reference [15] & 10,950.75 & 0.73\% & 195.63 \\ \hline MOBA-CV-SARSA with fixed range & 11,008.45 & 0.20\% & 143.85 \\ \hline \multicolumn{5}{c}{The total number of iterations is 200 times.} \\ \hline MOBA-CV-SARSA-MA & 11,020.47 & 0.1\% & 134.63 \\ \hline MOBA-CV-SARSA-DA & 11,031.05 & X & 214.02 \\ \hline \multicolumn{5}{c}{The total number of iterations is less than 200 times.} \\ \hline MOBA-CV-SARSA-DA & 11,023.87 & 0.07\% & 216.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparative analysis of hypervolume with dynamic range adjustment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Diversity Metric**} \\ \cline{2-5}  & **Average** & **Percentage** & **Standard Deviation** \\ \hline \multicolumn{5}{c}{The total number of iterations is 100 times.} \\ \hline \multirow{3}{*}{MOBA-CV} & the best \(\alpha\), \(\gamma\) & 0.2098 & 0.43\% & 0.0029 \\ \cline{2-5}  & reference [15] & 0.2101 & 0.57\% & 0.0023 \\ \hline MOBA-CV-SARSA with fixed range & 0.2089 & X & 0.0092 \\ \hline \multicolumn{5}{c}{The total number of iterations is 200 times.} \\ \hline MOBA-CV-SARSA-MA & 0.2101 & 0.72\% & 0.0030 \\ \hline MOBA-CV-SARSA-DA & 0.2095 & 0.29\% & 0.0101 \\ \hline \multicolumn{5}{c}{The total number of iterations is less than 200 times.} \\ \hline MOBA-CV-SARSA-DA & 0.2094 & 0.24\% & 0.0101 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparative analysis of diversity metric. In Table 18, despite MOBA-CV-ref exhibiting shorter latency than MOBA-CV-SARSA with a fixed range based on the reference settings, it consumes 195.314 W and 119.048 W more energy than the MOBA-CV derived from hyperparameter experiments. This vividly illustrates the performance enhancement achieved through reinforcement learning. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Times**} \\ \cline{2-3}  & **Number of Non-Dominated** & **Number of Non-Dominated** \\  & **Solutions Is Zero** & **Solutions Is Maximum** \\ \hline \multicolumn{3}{c}{The total number of iterations is 100 times.} \\ \hline \multirow{2}{*}{MOBA-CV} & the best \(\alpha\), \(\gamma\) & 0 & 12 \\ \cline{2-3}  & reference [15] & 1 & 6 \\ \hline MOBA-CV-SARSA with fixed range & 0 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparative analysis of 0 and MAX with dynamic range adjustment. Figure 11: The convergence curve of the hypervolume. Figure 12: The convergence curve of the diversity metric.