## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.1 Edge Computing Constraints  
In Table 4, MOBA-CV without reinforcement learning, while considering constraints, enhanced the hypervolume indicator by 4.68% and 1.54% compared to NSGA-II-CV and MOPSO-CV, respectively, across 30 experiments. Despite MOBA-CV displaying a higher standard deviation than the other methods, it outperformed NSGA-II-CV even in the worst experiment, achieving a hypervolume value of 10,599.12. Thus, despite more significant variability, MOBA-CV remains the most effective among the three methods. Furthermore, in Table 5, MOBA-CV showed a 2.72% improvement over NSGA-II-CV and a 1.05% improvement over MOPSO-CV regarding the diversity indicator. In comparing non-dominated sorting over 30 iterations, none of the methods yielded a \"retained non-dominated solution count equals 0\", indicating each method's successful discovery of non-dominated solutions. MOBA-CV consistently retained the highest number of \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Hypervolume**} \\ \cline{2-4}
**Method** & **Average** & **Percentage** & **Standard Deviation** \\ \hline MOBA-CV & 10,967.27 & X & 190.33 \\ \hline NSGA-II-CV & 10,453.76 & 4.68\% & 97.8 \\ \hline MOPSO-CV & 10,798.6 & 1.54\% & 118.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of hypervolume without reinforcement learning. \begin{table}
\begin{tabular}{l c} \hline \hline  & **Parameters** & **Value** \\ \hline Channel power gain at a reference distance of 1 m and a & 0.1 \\ transmitting power of 1 W, g\({}_{0}\) & 100 \\ \hline Population size, npop & 100 \\ \hline Dimension of bat, N & 98 \\ \hline Loudness, A\({}_{i}\) & \([0,1]\) \\ \hline Pulse rate, r\({}_{i}\) & \([0,1]\) \\ \hline Initial pulse rate, r\({}_{0}\) & 0.6 \\ \hline f\({}_{\min}\) & 0 \\ \hline f\({}_{\max}\) & 2 \\ \hline Iterations & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l c} \hline \hline  & **Value** \\ \hline Ir, learning rate & 0.3 \\ \hline df, decay factor & 0.9 \\ \hline \(\varepsilon\) of \(\varepsilon\)-greedy & \([0,1]\) \\ \hline \(\Delta\varepsilon\) of \(\varepsilon\)-greedy & 0.04 \\ \hline \(\omega_{1}\) of reward & 0.25 \\ \hline \(\omega_{2}\) of reward & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Configuring parameters for the SARSA. non-dominated solutions, as evidenced by the \"retained non-dominated solution count equals the most\" in each comparison, underscoring its superior performance, as depicted in Table 6.  
Table 7 displays the outcomes of the optimal solutions identified by three methods concerning total network energy consumption and latency. Latency and energy consumption represent conflicting objectives; however, when latency is constrained within acceptable limits, MOBA-CV achieves reductions of 77.543 W and 34.722 W in energy consumption compared to NSGA-II-CV and MOPSO-CV, respectively. These results unmistakably indicate MOBA-CV's capability to attain a more harmonized solution amidst such trade-off dilemmas. Additionally, Table 8 outlines the mean computation time for each approach. MOBA-CV exhibits computations of 3.562 s and 642.416 s, making it swifter than NSGA-II-CV and MOPSO-CV, respectively. Given the context of edge computing, shorter computation time aligns better with optimizing task-offloading determinations in this domain. In Figure 10, we can clearly observe the comparison results after non-dominated sorting. MOBA-CV retains 41 non-dominated solutions, and NSGA-II-CV and MOPSO-CV have 9 and 24 solutions, respectively. Obviously, MOBA-CV not only obtains the largest number of non-dominated solutions, but more importantly, these solutions achieve a balance between delay time and energy consumption. These solutions are mainly concentrated in the center of the graph. In comparison, the solutions obtained by other comparison methods are mainly concentrated in the upper left and lower right corners.