# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 4 HMAO Algorithms
### Routing Phase
Consequently, it experiences higher packet loss when the UAV density increases because of its inability to predict movements and

Figure 4: Count of CHs vs. number of UAVs. Figure 5: Comparison of CH changing ratios. maintain stable paths. DCM adjusts clusters based on local metrics but fails to adapt routing decisions to network-wide changes, leading to a decrease in PDR as the number of UAVs increases, owing to inefficient handling of mobility. MFO focuses on optimizing clustering through moth-inspired search patterns; however, it lacks the routing efficiency and global adaptability of HMAO, leading to increased packet loss in highly mobile conditions where stable routing is crucial. DCFH improves clustering with the fire hawk optimizer, but its routing mechanisms are reactive, not predictive. Thus, DCFH suffers from packet loss with high UAV mobility, for which proactive path selection is required. Figure 6 shows that with an increased number of UAVs, the HMAO algorithm proposed here offers a higher PDR than the other methods. This is because of its stable clustering and routing, which reduce packet loss in dynamic environments. With an increased number of UAVs, HMAO has low routing overhead, as depicted in Figure 7. This is because once stable clusters are merged by using MGO with adaptive routing having AO, there arises a low-frequency update of routes as compared with a regular communication process with routing overhead. The local information of HMAO can be used for the fine-tuning of clustering, which may reduce the frequency update, resulting in a need for no re-clustering. Although HMGOC results in efficient clustering, the scheme still suffers from more significant routing overhead because there is no global optimization layer, such as AO. AO can use global information and perform congestion-aware routing, whereas HMGOC is less capable of predicting UAV density and mobility changes, leading to more route adjustments and, in turn, more overhead. MWCRSF uses mobility metrics for better initial cluster formation, but it lacks the global awareness that HMAO has in optimizing routes based on network-wide conditions. This leads to increased overhead because UAV movements cause repeated rerouting efforts. DCM dynamically adjusts clusters, but fails to manage overhead when the number of UAVs increases. It lacks a mechanism for prediction and reacts only after changes occur, thereby increasing the control message exchange and overhead. MFO performs well in initial clustering but has a relatively higher overhead in routing compared to HMAO because it does not utilize global data for proactive adjustments, and hence, more frequent recalculations of the route. DCFH adapts to clustering based on local conditions but does not account for a global view in routing. Therefore, it brings about more overhead because it has to update routes frequently owing to difficulty adapting and coping with alterations in different network densities and UAV movement patterns accordingly. Figure 6: PDR vs. number of UAVs. Figure 8 indicates that, with a higher number of UAVs, the latency is less than that of HMAO. The reason for this phenomenon is that the AO predicts its routes and makes one that is not congested, thereby resulting in less delay. HMAO reaches a low latency because of the predictive capability of AO, which opts for routes that, based on changing numbers of UAVs over time, would not be different. Thus, with an increase in the number of UAVs in a network, data delivery also occurs quickly because it adjusts routing in proportion to the changes predicted by UAVs. HMGOC is good at selecting a stable head of the cluster, but is also a high-latency scheme owing to its reactive routing behavior. In the absence of predictive routing mechanisms, such as AO, it lags in adapting to changes in the network. The reduced latency initially owing to the use of mobility-based clustering makes MWCRSF weak in high-mobility environments where the routing path keeps changing.