## 5 Review of Case Studies: Flying Vehicle Routing Optimization  
Several case studies have been conducted to explore the application of GAs for flying vehicle routing optimization. These studies aim to address the unique challenges associated with routing UAVs or drones, considering factors such as airspace regulations, battery limitations, and varying mission objectives. The distribution of these studies considered in this work over the years is depicted in Figure 5.  
One notable case study by [PERSON] et al. [6] proposes a GA-based routing (GAR) protocol for flying ad hoc networks (FANETs) that addresses the challenges of high node mobility. The protocol improved routing by considering factors such as link stability, link bandwidth, and node energy. The selection, crossover, and variation operators of the GA are enhanced to optimize route planning. Experimental results demonstrated that GAR significantly improves the network throughput, reduces the delay, and enhances the network stability, making it well suited for FANETs. However, the paper acknowledged the need for future research to consider energy consumption in realistic environments. However, [PERSON] et al. [54] introduced two methods for the path planning of UAVs in a real 3D environment. One method was based on the A-Star algorithm and provides real-time path planning capability, while the other method used a GA to generate multiple trajectories based on a Pareto front. Both methods considered the dynamic properties of the UAV and computed 2D trajectories, with the missing dimension being computed using a recursive algorithm. The A-Star method has an average computation time of 836 ms, demonstrating its real-time capability, while the GA method offers performance and flexibility by identifying failures in the tactical situation and providing diverse solutions. The experimental results show that the GA improves the fitness of the A-Star solution by an average of 44.5%. The paper concluded by highlighting the potential of combining these methods to create a fully operational system, and suggests future work to improve the quality of the estimator and explore parallelization techniques. [PERSON] et al. [55] proposed a quality-aware coverage and path planning scheme for UAV networks in complex environments. They introduced a waypoint generation algorithm to achieve full coverage with satisfactory spatial resolution and an energy-efficient path planning solution for multiple UAVs. Simulation results demonstrated improved coverage and energy consumption compared to traditional techniques. They suggested two future research directions: exploring online adjustment strategies for unpredictable situations and investigating effective communication mechanisms for delivering sensed images to  
Figure 5: Distribution of GA-based FV routing optimization studies. end-users. The proposed framework will be extended to incorporate relay UAV nodes and consider image quality and deadline requirements. A study of [PERSON] et al. [56] presented a study on the integration of wind effects in the optimization of task allocation and path planning for fixed-wing UAVs. Their objective is to minimize the time required for UAVs to complete tasks in the presence of steady wind. The authors proposed a variable-speed Dubins path VRP (VS-DP-VRP) model that incorporates the minimum turning radius of UAVs and calculates the UAV's ground speed based on the wind speed and airspeed. The model is solved using a GA with customized crossover and mutation operators. The feasibility and effectiveness of the method are demonstrated through an illustrative example, and the sensitivity of the algorithm to different parameters is analyzed. The study concluded that the proposed model and algorithm provide an effective solution for UAV task allocation and path planning under steady wind conditions. However, [PERSON] and [PERSON] [57] presented a modular approach for generating state trajectories for a miniature UAV while considering computational constraints, obstacle avoidance, and priority assignment.