# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 2 Related Works  
As mentioned in the previous section, meta-heuristics play an important role in the research field of FANETs' routing protocols and energy optimization. Therefore, this section aims to present some recent studies that discuss the application of meta-heuristics in FANETs. Ref. [40] proposed an ad hoc on-demand multi-path distance vector routing protocol-based elephant-herding optimization to maximize the network lifetime. Ref. [41] constructed an ad hoc on-demand multi-path distance vector routing protocol using an algorithm for selecting a consistent path to improve the performance of quality of service (QoS) in high-speed MANETs.  
The FF-AOMDV, which introduces an approach for selecting an efficient path that achieves both the shortest distance and the lowest energy consumption, was proposed in [42]. Similar to the AOMDV, in the event of a link disruption, the source node in the FF-AOMDV will utilize the next shortest path to the destination node from the routing table. However, compared to the AOMDV, although the FF-AOMDV considers both energy and shortest distance criteria, its transmission performance is not high, and the improvement in the network lifetime is not significant.  
Ref. [43] proposed the QoS-AOMDV protocol, which is based on the AOMDV and aims to enhance QoS support. The QoS-AOMDV obtains high-quality paths by acquiring information on the queue length and remaining energy through cross-layer interactions. However, data collisions result in increased end-to-end delays and decreased packet delivery rates. [PERSON] et al. [44] proposed a multi-path routing protocol that ensures link stability. The protocol defines the link stability probability based on both the mobility model and queue length. This approach effectively balances the distance between nodes and the routing lifetime, thereby enhancing link stability. Nevertheless, due to frequent path changes, a larger number of nodes are involved in maintaining network stability, resulting in higher energy consumption for the nodes.  
In [45], a study was conducted on the problem of selecting the optimal path using a hybrid optimization approach based on the 2-Opt algorithm and the Artificial Bee Colony (ABC) algorithm. The 2-Opt algorithm is an optimization method commonly used for solving the Traveling Salesman Problem, while the ABC algorithm simulates the foraging behavior of honeybees to find optimized solutions. To address the path selection problem, fuzzy rules were employed to classify nodes based on their end-to-end delay, thus preventing potential data packet losses that could occur when nodes are on the verge of leaving the network. The simulation results indicate that the proposed fuzzy rule selection in combination with the ABC-2 Opt algorithm offers limited improvements in terms of the packet delivery rate and malicious node detection rate compared to the rule selection using the ABC algorithm.  
A protocol called the expected remaining lifetime-based AOMDV (ERL-AOMDV) is proposed in [46]. In the latest developments of the AOMDV (ad hoc on-demand multipath distance vector) protocol, the MMRE-AOMDV (minimum-maximum residual energy AODDV) stands out as the most advanced approach from an energy efficiency perspective. The ERL-AOMDV (energy and residual lifetime AOMDV) goes even further in-depth in studying a node's residual energy. It emphasizes selecting the optimal path by continuously considering the remaining energy of nodes, estimating the time needed for completing communication sessions, and taking into account the expected remaining lifetime of nodes. The ERL-AOMDV utilizes three optimal paths to send data packets. Although the ERL-AOMDV showed significant improvements in performance across different node densities, it did not take into account performance under varying UAV velocities.