## 4 Simulation Results
### Quantitative Analysis  
In this section, the performance of the proposed MGOA-WAVLCN is analyzed in terms of throughput, PDR, routing overhead, and end-to-end delay. In addition, the performance is analyzed with different optimization algorithms; Particle Swarm Optimization (PSO) algorithm, Grey Wolf Optimization (GWO) algorithm, FSA, GOA, and MGOA on different numbers of nodes such as 50, 100, 250, and 500. By investigating Table 3, the proposed routing protocol, MGOA, obtained better throughput compared to other optimization algorithms. As seen in Table 3, the routing protocol MGOA obtained 594, 589, 553, and 544 bits per seconds on 50, 100, 250, and 500 nodes, respectively, which are better than other comparative algorithms. Additionally, the graphical analysis of MGOA-WAVLCN in terms of throughput is represented in Figure 3.  
Correspondingly, in Table 4, the performance of the proposed MGOA-WAVLCN is analyzed by means of PDR. By inspecting Table 4, the proposed routing protocol MGOA achieved PDR of 93.28%, 90.12%, 88.90%, and 84.10% on 50, 100, 250, and 500 nodes, respectively. Compared to the existing optimization algorithms, the proposed routing protocol MGOA showed a maximum of 16.69% and minimum of 2.2% improvement in PDR. In this research study, the proposed routing protocol MGOA considered appropriate fitness function, which effectively avoids the packet loss during route generation. Graphical analysis of MGOA-WAVLCN in terms of PDR is indicated in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Throughput (bit/s)**} \\ \hline \multirow{2}{*}{**Optimization Algorithm**} & \multicolumn{4}{c}{**Number of Nodes**} \\  & **50** & **100** & **250** & **500** \\ \hline PSO & 531 & 520 & 499 & 480 \\ GWO & 564 & 532 & 521 & 502 \\ FSA & 572 & 555 & 539 & 528 \\ GOA & 588 & 573 & 540 & 531 \\ MGOA & 594 & 589 & 553 & 544 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance analysis of MGOA-WAVLCN in terms of throughput. The performance analysis of MGOA-WAVLCN in terms of routing overhead is depicted in Table 5. By inspecting Table 5, the proposed routing protocol MGOA obtained 262, 269, 278, and 283 routing overhead on 50, 100, 250, and 500 nodes, respectively, which are effective compared to other routing protocols such as PSO, GWO, FSA, and GOA. The overall experimental outcome showed that the relation between the average overhead and the route maintenance parameter are straightforward. Therefore, many route discovery attempts were done to find dynamic routing between source and destination nodes. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**PDR (\%)**} \\ \hline \multirow{2}{*}{**Optimization Algorithm**} & \multicolumn{4}{c}{**Number of Nodes**} \\  & **50** & **100** & **250** & **500** \\ \hline PSO & 78 & 74.89 & 72.21 & 69 \\ GWO & 80 & 76.90 & 75 & 72 \\ FSA & 85 & 83.34 & 79 & 72.78 \\ GOA & 88 & 87.92 & 81 & 79.98 \\ MGOA & 93.28 & 90.12 & 88.90 & 84.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis of MGOA-WAVLCN in terms of PDR. Figure 4: Graphical analysis of MGOA-WAVLCN in terms of PDR. Figure 3: Graphical analysis of MGOA-WAVLCN in terms of throughput. graphical analysis of MGOA-WAVLCN in terms of routing overhead is represented in Figure 5.  
Similarly, the performance analysis of MGOA-WAVLCN in terms of end-to-end delay is stated in Table 6. By inspecting Table 6, the proposed routing protocol MGOA consumed only a less time for transmitting the data packets from source to the destination nodes. The proposed routing protocol MGOA consumed 2.09, 2.12, 2.19, and 2.31 milliseconds on 50, 100, 250, and 500 nodes, respectively, which are better compared to other optimization algorithms. Numerical analysis showed that the routing protocol MGOA approximately minimized 0.80 of a millisecond in relation to other routing protocols. The graphical analysis of MGOA-WAVLCN in terms of end-to-end delay is indicated in Figure 6.