## 3 Results and Discussion
In Scenario 1, the distribution of nodes appears to be relatively uniform or less complex, which favors an algorithm that focuses on efficient exploitation. GWO's balance between exploration and exploitation allows it to quickly converge to an optimal solution without the need for the additional exploratory mechanisms present in EGWO. As a result, GWO slightly outperforms EGWO in this scenario. Similarly, in Scenario 6, where nodes are distributed around three distinct centroids, the search space exhibits well-defined attractive regions. GWO's aggressive exploitation strategy is particularly effective in such structured environments, as it rapidly fine-tunes the solution near these centroids. In contrast, EGWO's enhanced exploration capability, while beneficial in more complex or multimodal scenarios, may slightly delay convergence in this case, leading to a marginally higher power transmission value compared to GWO. Table 6 shows the average values of optimal power transmission, converted from dBm to microwatts (\(\upmu\)W), for four optimization algorithms across six different scenarios. In Scenario 1, GWO achieves the lowest average power transmission at \(11.867\)\(\upmu\)W, followed by EGWO at \(12.557\)\(\upmu\)W, PSO at \(14.709\)\(\upmu\)W, and BESO at \(30.623\)\(\upmu\)W, indicating GWO's effectiveness in reducing power when the node distribution is relatively uniform. In Scenario 2, EGWO slightly outperforms GWO by achieving an average power transmission of \(15.441\)\(\upmu\)W compared to GWO's \(15.575\)\(\upmu\)W, while PSO and BESO record higher values of \(22.504\)\(\upmu\)W and \(52.543\)\(\upmu\)W, respectively. In Scenario 3, EGWO clearly leads by attaining the lowest power transmission of \(7.556\)\(\upmu\)W, followed by GWO at \(12.467\)\(\upmu\)W, PSO at \(16.559\)\(\upmu\)W, and BESO at \(35.188\)\(\upmu\)W. Similarly, in Scenario 4, EGWO achieves the best performance with \(13.636\)\(\upmu\)W, while GWO, PSO, and BESO register \(18.642\)\(\upmu\)W, \(21.411\)\(\upmu\)W, and \(21.834\)\(\upmu\)W, respectively. In Scenario 5, EGWO again slightly outperforms GWO, with average power transmissions of \(21.591\)\(\upmu\)W and \(21.631\)\(\upmu\)W, respectively, while PSO and BESO record \(22.793\)\(\upmu\)W and \(81.652\)\(\upmu\)W. Finally, in Scenario 6, GWO attains the lowest transmission at \(4.332\)\(\upmu\)W, followed closely by EGWO at \(4.689\)\(\upmu\)W, whereas PSO and BESO record \(5.946\)\(\upmu\)W and \(31.872\)\(\upmu\)W, respectively. This analysis indicates that GWO consistently performs best in Scenarios 1 and 6, while EGWO demonstrates marginally superior performance in Scenarios 2 and 5 and clearly outperforms the other algorithms in Scenarios 3 and 4. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & \multicolumn{6}{c}{**Scenario**} \\ \cline{2-7}  & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) & \(\mathbf{6}\) \\ \hline BESO & \(-15.139\) & \(-12.794\) & \(-14.536\) & \(-16.608\) & \(-10.880\) & \(-14.965\) \\ PSO & \(-18.323\) & \(-16.477\) & \(-17.809\) & \(-16.693\) & \(-16.421\) & \(-22.257\) \\ GWO & \(-19.256\) & \(-18.075\) & \(-19.042\) & \(-17.295\) & \(-16.649\) & \(-23.632\) \\ EGWO & \(-19.011\) & \(-18.113\) & \(-21.216\) & \(-18.652\) & \(-16.657\) & \(-23.288\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Power transmission in dBm.