## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Minimum** & **Maximum** & **Mean** \\ \hline BESO & \(10.429456\) & \(20.312532\) & \(14.171138\) \\ PSO & \(11.898477\) & \(33.346832\) & \(18.473567\) \\ GWO & \(13.291528\) & \(21.313970\) & \(16.461065\) \\ EGWO & \(13.619340\) & \(21.6570003\) & \(19.445919\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Execution Time of each algorithm. Figure 6: Power transmission values for each node in Scenario 1.  
perform well, with PSO often achieving results close to GWO. However, GWO demonstrates a clear advantage in achieving lower power transmission for most nodes, as seen in Figure 7.  
In Scenario 1, GWO achieves the lowest average power transmission, followed by EGWO, PSO, and BESO, indicating GWO's effectiveness in reducing power for this scenario. Similarly, in Scenario 2, EGWO and GWO attain the lowest average power transmission with very similar values, while PSO and BESO register notably higher transmissions. In Scenario 3, EGWO clearly leads by achieving the lowest average power transmission, with GWO and PSO following, and BESO recording the highest value. In Scenario 4, EGWO again records the lowest transmission, with GWO next; meanwhile, BESO shows significant improvement by nearly matching PSO, which exhibits the highest power transmission (i.e., the least efficient performance) in this scenario. In Scenario 5, where nodes are deployed in a circular pattern, EGWO obtains the lowest average power transmission, followed closely by GWO and PSO, whereas BESO continues to exhibit the highest value. Finally, in Scenario 6, with nodes distributed around three centroids, GWO achieves the lowest average power transmission, followed by EGWO, PSO, and BESO. This analysis indicates GWO's consistent performance in most scenarios. Table 5 shows the average value of optimal power transmission in dBm for six different scenarios, using four optimization algorithms: BESO, PSO, GWO, and EGWO. In Scenario 1, GWO achieves the lowest average power transmission with \(-19.256\) dBm, followed by EGWO at \(-19.011\) dBm, PSO at \(-18.323\) dBm, and BESO at \(-15.139\) dBm, indicating GWO's effectiveness in reducing power for this scenario. In Scenario 2, EGWO slightly outperforms GWO by achieving an average power transmission of \(-18.113\) dBm compared to GWO's \(-18.075\) dBm, while PSO and BESO record \(-16.477\) dBm and \(-12.794\) dBm, respectively. In Scenario 3, EGWO clearly leads by attaining the lowest power transmission of \(-21.216\) dBm, followed by GWO at \(-19.042\) dBm, PSO at \(-17.809\) dBm, and BESO at \(-14.536\) dBm. Similarly, in Scenario 4, EGWO achieves the least power transmission \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Minimum** & **Maximum** & **Mean** \\ \hline BESO & \(-2.357431\) & \(4.919863\) & \(2.214195\) \\ PSO & \(-7.926426\) & \(-2.605397\) & \(-3.725375\) \\ GWO & \(-9.274447\) & \(3.547884\) & \(-0.614383\) \\ EGWO & \(-9.006406\) & \(0.953669\) & \(-4.150733\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best Fitness of each algorithm. Figure 7: Power transmission in 6 scenarios. with \(-18.652\) dBm, while GWO, PSO, and BESO register \(-17.295\) dBm, \(-16.693\) dBm, and \(-16.608\) dBm, respectively. In Scenario 5, EGWO again slightly outperforms GWO, with average power transmissions of \(-16.657\) dBm and \(-16.649\) dBm, respectively, while PSO and BESO record \(-16.421\) dBm and \(-10.880\) dBm. Finally, in Scenario 6, GWO attains the lowest average power transmission at \(-23.632\) dBm, followed closely by EGWO at \(-23.288\) dBm, while PSO and BESO register \(-22.257\) dBm and \(-14.965\) dBm, respectively. This analysis indicates GWO's consistent performance in most scenarios, with EGWO demonstrating marginally superior results in Scenarios 2 and 5 and clearly outperforming the other algorithms in Scenarios 3 and 4, while GWO excels in Scenarios 1 and 6.