## 4 Discussion  
The results presented in Section 3 allow us to visualize the performance of the proposed CWGO, DE-SEP, E-CERP, DUCISCA, and EECHIGOWo algorithms on the selection of CHs and routing-path optimization in various WSN configurations using a unified performance metric. The WSNs were simulated for multiple rounds and multiple numbers of nodes in both static and dynamic scenarios across four different wireless technologies, namely LoRa, Wi-Fi, Zigbee, and BLE. During the static WSN simulation for LoRa technology, the proposed CWGO algorithm consistently outperforms other algorithms in 100- and 1000-sensor node scenarios. While all algorithms show a decline in fitness over time, the CWGO's decline in performance is the most gradual, indicating its consistency as time progresses. This performance is also evident for 1000 sensor nodes, indicating its robustness and scalability even for a larger number of sensor nodes. In contrast, the EECHIGOWo algorithm shows the lowest fitness value across the simulation rounds for both 100 and 1000 sensor node configurations. The performance of each algorithm using Wi-Fi technology also demonstrates the proposed CWGO's superior performance against the other algorithms. For both 100 and 1000 sensor node configurations, CWGO starts with the highest fitness value and retains its lead across 2000 rounds. DUCISCA, EECHIGOWo, and DE-SEP show a significant decrease in performance at the latter end of the simulation, while E-CERP still lags behind the proposed CWGO by a considerable amount of fitness value. The results for Zigbee technology demonstrate the proposed CWGO algorithm's performance for low-power, short-range communication environments. CWGO's fitness remains consistently higher than that of the other algorithms despite the similarities in performance trends. While DUCISCA, E-CERP, and DE-SEP perform moderately, they cannot match the performance of CWGO, which shows better long-term network sustainability. As seen across other technologies, EECHIGOWo performs poorly, failing to maintain competitive fitness values due to its inefficiency in conserving energy in low-power Zigbee networks. In Bluetooth low energy (BLE) technology, the performance of all algorithms begins with closer fitness values, reflecting the challenges posed by BLE's low-power, energy-constrained environment. However, as the simulation progresses, the proposed CWGO algorithm gradually widens the performance gap against other algorithms. The dynamic WSN results are identical to those of the static WSN simulation. For instance, in LoRa technology, for both 100 and 1000 sensor node setups, the proposed CWGO algorithm begins with the highest fitness value, maintaining its lead across all 2000 rounds. This shows the CWGO's flexibility in complex dynamic environments where sensor node movements and increasing network density are present. EECHIGOWo again performed the worst, with its fitness value dropping sharply at the end of the simulation for 1000 nodes, while DUCISCA, DE-SEP, and E-CERP showed moderate performance. For Wi-Fi technology, the proposed CWGO continues to outperform other algorithms in both 100 and 1000 dynamic node configurations. The performance of each algorithm for Zigbee technology shows a sharper decline as the simulation round progresses. Similar to static nodes, a similar trend is observed across all algorithms, and the gap between them is lesser than in other wireless technologies. Lastly, for BLE technology, at 1000 sensor nodes, E-CERP and CWGO started with an almost identical fitness value, but their gap widens as \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Running Time (Seconds)** \\ \hline EECHIGOWo & 25.986 \\ \hline DUCISCA & 20.632 \\ \hline DE-SEP & 18.554 \\ \hline E-CERP & 16.215 \\ \hline Proposed CWGO & 13.548 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Running time for each algorithm. the simulation round increases, in which CWGO performs better.