# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 4 HMAO Algorithms
### Routing Phase
It suffers from a lack of a global perspective while routing, and thus delays adjustments and increases the latency. As the network size increases, the local updates of DCM for clustering create higher latency because it takes time to update the cluster structures and routes after the movement of the UAVs. The routing mechanisms of MFO are efficient but do not investigate global mobility patterns. Figure 8: Latency (s) vs. number of UAVs. Figure 7: Routing overhead vs. number of UAVs. As shown in Figure 9, HMAO consumes less energy than HMGOC, MWCRSF, DCM, MFO, and DCFH as its approach performs dual optimization for local and global optimization. Therefore, in HMAO, MGO selects energy-efficient, stable CHs based on the residual energy and mobility stability that reduce events of frequent re-clustering. At the same time, in AO, global information is used to route the packet, minimizing link failures and low re-routing. This saves the energy costs of transmission. Furthermore, load balancing and congestion-aware secondary path activation of HMAO reduce energy-intensive node overloading and offer smooth data delivery, such that energy is conserved compared to other methods that do not integrate these optimum mechanisms. Other algorithms, such as HMGOC, MWCRSF, DCM, MFO, and DCFH, are characterized by very high energy consumption, mainly because of the occurrence of repeated re-clustering and inefficient routing. In the HMGOC and MWCRSF algorithms, predictive routing and load balancing are not included, indicating that the stability of clusters is unstable and consumes considerable energy through repeated re-clustering processes. In the cases of DCM and DCFH, although dynamic clusters are supported, failure to utilize global information about the network results in frequent link breaks and re-routing, which leads to energy consumption. MFO has a simpler optimization strategy with larger data paths associated with higher transmission costs, whereas HMAO possesses attributes such as adaptive clustering and fault tolerance. ## 6 Conclusions

The high mobility and fast-changing nature of UAVs bring several challenges to clustering and routing in FANETs. In this study, we proposed a hybrid bio-inspired algorithm, HMAO, which combines the strengths of MGO and AO to enhance clustering stability and data delivery reliability. Leveraging global information, HMAO integrates MGO for efficient CH selection and AO for routing, achieving load balancing and fault-tolerant path selection. Through local optimization, MGO maintains stable CHs by considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbors, reducing re-clustering frequency, and ensuring efficient cluster coordination. For clustering maintenance, HMAO employs a congestion-based approach that redistributes UAVs in overloaded clusters, addressing communication imbalances. Its predictive routing mechanism uses mobility data to establish reliable and adaptive multi-hop paths, minimizing packet loss in high-mobility scenarios. Additionally, fault-tolerant secondary path activation optimizes

Figure 9: Average energy consumption (J) vs. number of UAVs. energy consumption while ensuring resilient data transfer. HMAO's density-aware clustering further enhances adaptability, enabling efficient UAV resource management under varying network loads. Simulation results demonstrate that HMAO outperforms existing protocols in terms of PDR, delay, overhead, and energy consumption, making it a scalable and high-performance solution for dynamic FANETs. Despite its proven advantages, HMAO has some limitations that create avenues for further research. A primary drawback of HMAO is that it relies heavily on real-time network condition assessments, which can be problematic in highly congested networks and resource-constrained environments. Further improvements could also be made to how HMAO adapts to extreme mobility patterns, such as swarm-based UAV formations, for better response times under dynamic network topology changes.