## 1 Introduction
### Related Work
The proposed method is implemented in MATLAB to find solutions for min-max MDVRP with varying numbers of vehicles and customer locations. A comparative analysis is conducted to assess the performance of the proposed algorithm against a linear programming (LP)-based algorithm from the existing literature in terms of solution optimality. It is demonstrated that the ACO method proposed in the paper yields more optimal results compared to the current LP-based approach through simulations and statistical evaluations. A recent study [44] discusses a min-max variation of the Dubins multiple traveling salesman problem (mTSP). This issue naturally appears in mission planning applications that involve fixed-wing UAVs. Initially, the routing problem, which is known as the one-in-a-set Dubins mTSP problem (MD-GmTSP), is formulated as a mixed-integer linear program (MILP). Subsequently, heuristic-based search methods are developed for the MD-GmTSP using path construction algorithms to find initial possible solutions. Afterwards, these solutions are enhanced using variations of the variable neighborhood search (VNS) meta-heuristic. Additionally, a graph neural network to implicitly learn policies is investigated for the MD-GmTSP using a learning-based approach. The results indicate that learning-based approaches are effective for smaller instances, whereas the VNS-based heuristics yield the optimal solutions for larger instances. The purpose of a recent research paper [45] was to establish the minimum number of unmanned aerial vehicles (UAVs) required for conducting aerial reconnaissance in a specific area. Initially, the energy consumption of a UAV flying at a constant speed was analyzed to determine the percentage of energy consumed per meter. By taking into account the length of the UAV's trajectory, the necessary energy percentage for completing a single tour was calculated. If it was found that a single UAV could not complete its designated trajectory, additional UAVs were added until each one could successfully finish its route. The study utilized the vehicle routing problem approach to determine the UAV trajectories, employing the genetic algorithm method as a metaheuristic optimization technique to solve the vehicle routing problem (VRP). The algorithm was implemented in the MATLAB environment, with adjustments made to the crossing rate and population number parameters in the genetic algorithm (GA) method to identify the minimum number of UAVs required for aerial observation in the target area and to optimize the UAV trajectories. In this study, the control of a quadcopter-type UAV was firstly performed by using FOPID controllers. The parameters of the controllers were obtained by using FHO, FFO, and PO algorithms. The controller that provided the best performance was compared with the performance of the SDCS controller. In this study, it was proved that the symbolic SDCS approach can be effectively used for the nonlinear model of the quadcopter UAVs, filling an important gap in this field. Furthermore, it provides much higher precision in achieving specified objectives compared to traditional machine learning approaches. The second purpose of this study is to investigate the optimal path planning conditions of the quadcopter. Thus, the path planning performances of the tested MH algorithms and the SDCS method were tested and compared. The optimal solution of the TSP for a single quadcopter and min-max TSP with multiple depots for multi quadcopters were obtained. The major contribution in this field is the reduction in energy and time costs while achieving higher levels of accuracy.