## 4 Experimental Results
### Parameter Sensitivity Analysis  
In order to evaluate the robustness and stability of the proposed hybrid optimization framework, a sensitivity analysis was conducted on three critical hyperparameters used in the reinforcement-learning (Q-learning) component: the learning rate \(\eta\), the discount factor \(\delta\), and the exploration rate \(\epsilon\). These parameters directly influence how the Q-values are updated and how the agent balances exploration versus exploitation during mutation decisions within the genetic algorithm loop.  
The goal of this analysis is to assess how variations in these parameters affect the final performance of the hybrid system. Each parameter was varied independently while keeping the remaining two fixed at their default values. The performance was measured using the final fitness score obtained after a fixed number of generations (150) under the same mission scenario and objective function as detailed in Section 3.1. This ensures consistency with the results reported in Section 4.1 and Table 3.  
The results of the analysis are summarized in Table 4. The fitness scores reflect the cumulative objective performance incorporating area coverage, node connectivity, and communication efficiency.  
As seen in Table 4, the learning rate \(\eta\) of 0.1 yielded the highest final performance, suggesting a balanced update speed is most effective in this context. Increasing the learning rate to 0.2 slightly decreased performance, likely due to the instability in value updates. Similarly, lower learning rates (e.g., 0.05) resulted in a slower convergence.  
The discount factor \(\delta\) had a more moderate impact. The best performance was observed at \(\delta=0.9\), which appropriately balances immediate and future rewards. Performance slightly declined at extreme values (0.7 and 0.99), though not as sharply as with \(\eta\) and \(\epsilon\).  
The exploration rate \(\epsilon\) had a more pronounced effect. Lower exploration rates (0.1) led to premature convergence and suboptimal solutions, while higher values (0.5) introduced excessive randomness. An intermediate setting (0.3) produced the best overall outcome.  
These findings demonstrate that the hybrid method is sensitive to its internal learning parameters and highlight the importance of tuning them carefully. This preliminary analysis also opens up potential directions for future work involving multi-parameter optimization and adaptive parameter adjustment during runtime.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Final Fitness** \\ \hline Learning Rate (\(\eta\)) & 0.05 & 1450 \\ \hline Learning Rate (\(\eta\)) & 0.10 & 1610 \\ \hline Learning Rate (\(\eta\)) & 0.20 & 1585 \\ \hline Discount Factor (\(\delta\)) & 0.70 & 1505 \\ \hline Discount Factor (\(\delta\)) & 0.90 & 1610 \\ \hline Discount Factor (\(\delta\)) & 0.99 & 1600 \\ \hline Exploration Rate (\(\epsilon\)) & 0.10 & 1400 \\ \hline Exploration Rate (\(\epsilon\)) & 0.30 & 1610 \\ \hline Exploration Rate (\(\epsilon\)) & 0.59 & 1550 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity analysis of Q-learning parameters. Final fitness scores are reported for each tested value after 150 generations.