# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 2 Related Works  
Related works highlight various approaches and advancements in clustering and routing techniques for FANETs. Although these previously proposed methods show significant improvements in energy efficiency, data delivery, and network stability, they lack scalability, adaptability to high mobility, and effective use of geographical information. A comparative analysis is thus provided to gain a clearer view of how our proposed HMAO algorithm fits the research. [PERSON] et al. [26] proposed energy-aware routing with virtual relay tunnels (EARVRT), which has the potential to control overheads for control messages using virtual relay tunnels for controlling updates better. Through this power-saving mechanism, redundant transmissions are avoided, thereby improving delivery quality and minimizing latency. Despite its applicability to medium-mobility FANETs, and that its performance is acceptable under controlled conditions, it lacks scalability because it is incompatible with larger networks and even highly dynamic environments such as UAVs. [PERSON] et al. [27] proposed a local filtering-based energy-aware routing scheme (LFEAR), which controls routing overhead while maintaining stable paths. The filtering method reduces the number of requests for routes and the amount of energy consumed, and maintains a higher degree of adaptability in medium-density networks. Despite its effectiveness in enhancing route stability, this strategy has resulted in an increased overhead in dense FANETs. In [28], the authors proposed a K-means-based mobility-aware routing protocol (K-MORP), where they utilized the K-means algorithm to generate clusters that help in efficient and scalable routing in mobility-intensive FANETs. Clustering simplifies the routing overhead by managing UAVs in groups, thus helping to allocate resources and reduce the complexity of routing. However, at higher UAV speeds, the dynamic clustering stability becomes a problem. Therefore, K-MORP is effective in controlled or semi-structured environments. This protocol is very useful in cases with moderate movement but very limited in high-mobility FANETs. In [29], the authors proposed a mobility-based weighted clustering scheme (MWCRSF) aimed at improving the routing efficiency in FANETs by optimizing weighted factors, such as node density and UAV mobility. Owing to its high computational complexity, the MWCRSF is not well suited to highly dynamic environments and has excellent scalability for large networks. [PERSON] et al., [30] proposed a dynamic clustering for fault handling (DCFH) method that targets the reliability of FANETs by dynamically adapting clusters to handle faults and ensure data integrity during communication. This approach is fault-tolerant because the network continues to work even if individual UAVs have connectivity problems. Using whale optimization algorithms (WOA) for FANETs, the authors in [31] proposed an intelligent clustering scheme (ICW). ICW consists of centralized clustering that uses WOA to determine the best cluster centers within a network. Using the closeness ratio, ICW determines which UAV belongs to which cluster, so that each UAV belongs to the cluster with the best closeness ratio. The closeness ratio indicates the relative proximity of nodes within a network. Typically, it is calculated as the reciprocal of the sum of the shortest path distances between each node in a network. In [32], the authors proposed a utility function-based greedy perimeter stateless routing method called the UF-GPSR protocol for FANETs. This method optimizes greedy forwarding strategies based on multiple vital UAV parameters, including the residual energy ratio, distance degree, movement direction, link risk degree, and speed. In recent years, bio-inspired algorithms have been used to solve the clustering problem in FANETs. Owing to their simplicity, effectiveness in solving complex optimization problems, and ability to avoid local minima, they have gained popularity.