# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
###### Abstract  
The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods. a 2020 20  
for multi-objective optimization to ensure cluster stabilization, promising predictability, and low latencies among routing features is required. All the clustering and routing algorithms developed so far have been found to be able to support specific aspects of FANET performance, but fail in their application in high-mobility environments where frequent re-clustering and route maintenance cause increased latency and relatively reduced network stability [20]. Most existing deployed methods rely on a few metrics, such as geographical positioning or residual energy, which are not sufficient for dynamic adaptive FANETs. This inability reflects the need for a better, multi-objective-based solution that can handle both clustering and routing dynamically [21]. The exploding nature of FANETs comprising UAVs has posed tremendous challenges in providing reliable, scalable, and energy-efficient communication. The problems associated with networks in FANETs are inherent because of the mobility of UAVs, changing topologies, and the associated frequent link failures and overhead communication [22]. The traditional approach to clustering has been proven to be crucial for the purpose of network scalability and reduction in energy consumption, but fails to account for dynamic network conditions. It mainly works based on local information, such as the residual energy or proximity. Thus, it fails to consider the cluster size. Re-clustering must be carried out every time the cluster head (CH) moves out of range, there is a drop in energy levels, or imbalances in the cluster size. Such highly imbalanced clusters lead to a high node overload in densely populated regions, causing congestion in communication [23]. Existing protocols are not efficient in scenarios involving mobility-induced link failure and efficient data delivery under high-density or high-mobility conditions. Energy consumption is inefficient, packet loss is high, and networks lack fault tolerance, leading to unreliable performance. Therefore, the adaptation and robustness of an algorithm to optimize clustering and routing in FANETs with global network information becomes of prime importance [24; 25]. To overcome these disadvantages, this paper proposes a novel hybrid bio-inspired algorithm based on the mountain gazelle optimizer (MGO) and aquila optimizer (AO), named HMAO, which combines local and global optimization techniques with the stringent requirements of FANETs. It uses the MGO algorithm for energy-efficient stable CH selection to ensure that the adaptation of the clustering process according to network conditions is still optimal in terms of energy usage.