## 4 Conclusions  
This study introduces an advanced learning approach, the multi-objective bat algorithm with reinforcement learning (MOBA-CV-SARSA), designed to optimize task-offloading decisions in edge computing. This optimization achieves a harmonious balance between network latency and energy consumption. By adhering to edge computing constraints and employing a tailored multi-objective function, task-offloading decisions for all devices can be concurrently determined. Compared to NSGA-RL-CV and QLPSO-CV, which integrate enhanced learning and share similar constraints, MOBA-CV-SARSA exhibits enhancements of 0.9% and 15.07% in hypervolume and 4.72% and 0.1% in the diversity metric. Additionally, it notably diminishes network energy usage within acceptable  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Latency Time (s)**} & \multicolumn{2}{c}{**Energy Consumption (W)**} \\ \cline{2-4}  & **Value** & **Difference** & **Value** & **Difference** \\ \hline MOBA-CV-SARSA-MA & 144.3 & X & 671.5 & 102.542 \\ \hline MOBA-CV-SARSA-DA & 148.4 & 4.066 & 569 & X \\ \hline \hline \end{tabular}
\end{table}
Table 19: Comparative analysis of latency and consumption with/without dynamic adjustment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Latency Time (s)**} & \multicolumn{2}{c}{**Energy Consumption (W)**} \\ \cline{2-4}  & **Value** & **Difference** & **Value** & **Difference** \\ \hline MOBA-CV-SARSA-MA & 144.3 & X & 671.5 & 102.542 \\ \hline MOBA-CV-SARSA-DA & 148.4 & 4.066 & 569 & X \\ \hline \hline \end{tabular}
\end{table}
Table 19: Comparative analysis of latency and consumption with/without dynamic adjustment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**Average Execution Time (s)**} \\ \hline \hline \multicolumn{2}{c}{The total number of iterations is 100 times.} \\ \hline \multirow{2}{*}{MOBA-CV} & the best \(\alpha\), \(\gamma\) & 7.373 \\ \cline{2-3}  & reference [15] & 7.317 \\ \hline MOBA-CV-SARSA with fixed range & 61.018 \\ \hline \multicolumn{2}{c}{The total number of iterations is 200 times.} \\ \hline MOBA-CV-SARSA-MA & 181.881 \\ \hline MOBA-CV-SARSA-DA & 180.434 \\ \hline \multicolumn{2}{c}{The total number of iterations is less than 200 times.} \\ \hline MOBA-CV-SARSA-DA & 131.821 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Comparative analysis of average execution time for different tuning mechanisms.  
latency limits. Leveraging enhanced learning for fine-tuning hyperparameters aids the MOBA in discovering optimal solutions. MOBA-CV-SARSA-DA outperforms its counterparts at the exact iteration count, validating the efficacy of dynamic adjustment ranges. Ultimately, the enhanced-learning-driven MOBA streamlines complex hyperparameter experiments and identifies the best trade-off solutions under set constraints.  
Conceptualization, C.-L.T. and C.-S.C.; methodology, C.-S.C.; software, Y.-H.S.; validation, C.-L.T., C.-S.C. and Y.-H.S.; formal analysis, C.-L.T.; investigation, C.-S.C.; resources, Y.-H.S.; data curation, C.-S.C.; writing--original draft preparation, Y.-H.S.; writing--review and editing, C.-S.C.; visualization, Y.-H.S.; supervision, C.-L.T.; project administration, C.-S.C.; funding acquisition, C.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project name: Integrated Development of Multimedia Advertising Marketing Combined with AI Data Analysis grant number 210A024.  
Not applicable for studies not involving humans or animals.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflict of interest.