## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.3 Dynamic Range Adjustment  
In this section, we explore the impact of dynamic tuning and its outperformance. Initially, MOBA-CV is introduced, focusing solely on constraint conditions. The optimal \(\alpha\) and \(\gamma\) values for this approach are determined through 8250 iterations of hyperparameter experiments, complemented by the optimal range values as referenced by [PERSON] et al. [15]. Subsequently, we introduce MOBA-CV-SARSA with a fixed range, utilizing a predefined search range, and MOBA-CV-SARSA-MA, which employs manual adjustment for a narrower search scope. Lastly, MOBA-CV-SARSA-DA dynamically adjusts the parameter ranges, incorporating an automatic switching mechanism. Table 13 presents various methods and their respective parameter configurations for the comparative experiment. The hypervolume indicator in Table 14 illustrates that under the sole consideration of edge computing constraints, MOBA-CV achieves superior performance when optimized with \(\alpha\) and \(\gamma\) values derived from experimental design, surpassing the performance of the MOBA-CV referenced in [15]. Furthermore, as in MOBA-CV-SARSA, augmenting MOBA-CV with reinforcement learning enhances its performance compared to MOBA-CV without such augmentation. Notably, MOBA-CV-SARSA-MA, which utilizes optimal solutions \(\alpha^{*}\) and \(\gamma^{*}\) obtained from 100 searches of MOBA-CV-SARSA with a fixed range, exhibits a 0.11% improvement over MOBA-CV-SARSA with a fixed range. Since MOBA-CV-SARSA-MA leverages the search outcomes of MOBA-CV-SARSA with a fixed range, \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameters** \\ \hline \multirow{3}{*}{MOBA-CV} & \(\alpha\): 0.03, \(\gamma\): 0.93 (8250 runs of hyperparameter experiment) \\ \cline{2-3}  & \(\alpha\): 0.9, \(\gamma\): 0.9 (reference [15]) \\ \hline \multirow{3}{*}{MOBA-CV-SARSA with fixed range} & \(\alpha\): [0, 1], \(\gamma\): [0, 1] \\  & \(\alpha\), \(\gamma\) difference is 0.01 \\ \hline \multirow{3}{*}{MOBA-CV-SARSA-MA} & \(\alpha\): [\(\alpha^{*}\) ], \([\alpha^{*}\) ] + 0.1], \(\gamma^{*}\): [\(\gamma^{*}\) ], \([\gamma^{*}\) ] + 0.1] \\  & \(\alpha\), \(\gamma\) difference is 0.006 \\ \hline \multirow{3}{*}{MOBA-CV-SARSA-DA} & \(\alpha\): [0, 1], \(\gamma\): [0, 1] \\  & \(\alpha\), \(\gamma\) difference is 0.01 \\ \cline{1-1} \cline{2-3}  & narrow search \\ \cline{1-1} \cline{2-3}  & \(\alpha\): [\([\alpha^{*}\) ], \([\alpha^{*}\) ] + 0.1] \\ \cline{1-1} \cline{2-3}  & \(\gamma\): [\(\gamma^{*}\) ], \([\gamma^{*}\) ] + 0.1] \\ \cline{1-1} \cline{2-3}  & \(\alpha\), \(\gamma\) difference is 0.006 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Configuring parameters for the MOBA with dynamic range adjustment. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Diversity Metric**} \\ \cline{2-4}  & **Average** & **Percentage** & **Standard Deviation** \\ \hline MOBA-CV-SARSA & 0.2098 & X & 0.0092 \\ \hline NSGA-RL-CV & 0.2197 & 4.72\% & 0.0016 \\ \hline QLPSO-CV & 0.21 & 0.1\% & 0.0027 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative analysis of 0 and MAX with reinforcement learning. this study integrates both approaches. It introduces an automatic switching mechanism in MOBA-CV-SARSA-DA. MOBA-CV-SARSA-DA employs two distinct strategies: (1) a total of 200 iterations for both fixed-range and small-range searches, and (2) 100 iterations for fixed-range searches combined with the same for small-range searches, resulting in a total of fewer than 200 iterations. Our analysis of the results reveals that when the total iteration count reaches 200, MOBA-CV-SARSA-DA exhibits a 0.095% enhancement in the hypervolume indicator compared to MOBA-CV-SARSA-MA. Moreover, MOBA-CV-SARSA-DA achieves performance akin to MOBA-CV-SARSA-MA without necessitating the completion of 200 iterations. In Table 15, MOBA-CV-SARSA with a fixed range showcases the most favorable diversity metric.