## 3 Results
### Algorithms Execution Time  
The execution time for each algorithm was evaluated to assess their computational efficiency in selecting cluster heads (CHs) and determining routing paths for data transmission. Execution time is a critical factor for algorithms used in wireless sensor networks (WSNs), as real-time or near-real-time performance is often required for many applications, especially in dynamic environments or resource-constrained settings. The running time presented in Table 11 was calculated by recording the start and end time of the CH selection and routing-path determination processes for each algorithm. It can be observed that for 50 sensor nodes with 500 rounds of simulation, the proposed CWGO algorithm demonstrated the fastest execution time at 13.548 s, highlighting its efficiency in real-time applications. E-CERP followed with 16.215 s, attributed to its simpler optimization process. DE-SEP and DUCISCA showed moderate execution times of 18.554 and 20.632 s, respectively, balancing performance and computational demands. EECHIGOW had the longest execution time at 25.986 s, reflecting the additional overhead from its iterative optimization and breeding processes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **EECHIGOWO** & **DUCISCA** & **DE-SEP** & **E-CERP** &
\begin{tabular}{} **Proposed** \\ **CWGO** \\ \end{tabular} \\ \hline
100 & 0.787118 & 0.912644 & 1.34219 & 1.120444 & 1.629341 \\ \hline
200 & 0.787118 & 0.912644 & 1.34219 & 1.120444 & 1.629341 \\ \hline
300 & 1.129613 & 1.247557 & 1.394609 & 1.638651 & 1.779021 \\ \hline
400 & 1.008761 & 1.162567 & 1.384479 & 1.635785 & 1.838343 \\ \hline
500 & 1.017553 & 1.287576 & 1.523731 & 1.726402 & 1.8676 \\ \hline
600 & 0.861086 & 1.330822 & 1.476195 & 1.675812 & 1.827526 \\ \hline
700 & 1.065576 & 1.225694 & 1.326939 & 1.712353 & 1.845613 \\ \hline
800 & 1.009266 & 1.201164 & 1.479747 & 1.717936 & 1.840102 \\ \hline
900 & 0.991727 & 1.164263 & 1.361375 & 1.617569 & 1.789958 \\ \hline
1000 & 0.420093 & 1.013169 & 1.17755 & 1.497468 & 1.67134 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Unified fitness table for BLE with dynamic nodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **EECHIGOWO** & **DUCISCA** & **DE-SEP** & **E-CERP** &
\begin{tabular}{} **Proposed** \\ **CWGO** \\ \end{tabular} \\ \hline
100 & \(-1.088405\) & \(-1.014428\) & \(-0.525501\) & \(-0.595907\) & \(-0.242875\) \\ \hline
200 & \(-0.245831\) & \(-0.107404\) & \(0.117948\) & \(0.468571\) & \(0.551786\) \\ \hline
300 & 0.129421 & 0.279013 & 0.394649 & 0.735413 & 0.882393 \\ \hline
400 & \(-0.137825\) & 0.172671 & 0.356507 & 0.688107 & 0.857051 \\ \hline
500 & \(-0.041443\) & 0.245884 & 0.483252 & 0.810451 & 0.930534 \\ \hline
600 & 0.421275 & 0.516029 & 0.634349 & 0.897887 & 1.12969 \\ \hline
700 & 0.491213 & 0.623307 & 0.760308 & 1.050573 & 1.140344 \\ \hline
800 & 0.114004 & 0.445772 & 0.720768 & 0.948353 & 1.10077 \\ \hline
900 & 0.55125 & 0.749592 & 0.879718 & 1.120389 & 1.246854 \\ \hline
1000 & 0.71005 & 0.824898 & 0.996566 & 1.19612 & 1.305493 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Unified fitness table for Zigbee with dynamic nodes.