## 4 Experimental Evaluation
However, there is one case (waypoint number = 40, population size = 50) in which the PO cannot find the optimal solution, while the SDCS calculated  
Figure 16: Trajectories generated by the PO algorithm when the population size is 100. Figure 17: Optimal trajectories generated by SDCS for three quadcopters. the optimal solution in all cases. In the case where the waypoint number is 40 and the population size is 50 for the MH algorithms, SDCS provided a better result by 1.231% than the result obtained with the PO algorithm. In the literature, the superiority of the SDCS was proven within the scope of the vehicle routing problem that was defined for the path planning of the cargo delivery quadcopters [30]. The results of this study are in line with the literature and support that SDCS provides the optimal solutions in path planning problems defined for the quadcopters. The performances of the tested MH algorithms and SDCS method were compared within the scope of min-max TSP with multiple depots for multi quadcopters. Considering Table 10, which includes optimal paths for two quadcopters, the PO provided the optimal solution among the MH algorithms. However, no MH algorithm has been able to achieve the optimal result, while the SDCS method provided the optimal and the exact solution in all cases. In Case 1 (waypoint number = 50 and the population size = 50), SDCS provided a better result by 4.506% than the result obtained with the PO algorithm, which is the best among the MH algorithms for this case. In Case 2 (waypoint number = 50 and population size = 100), the SDCS method achieved a 4.506% improvement over the result obtained with the PO algorithm by 3.176%. In Case 4 (waypoint number = 100 and population size = 100), the SDCS method achieved a 1.664% improvement over the result obtained with the PO algorithm as well. Considering Table 11, which includes optimal paths for three quadcopters, the FFO algorithm provided the optimal solution among the MH algorithms. However, no MH algorithm has been able to achieve the optimal result, while the SDCS method provided the optimal and the exact solution in all cases. In Case 1 (waypoint number = 50 and the population size = 50), SDCS provided a better result by 2.219% than the result obtained with the FFO algorithm, which is the best among the MH algorithms for this case. In Case 2 (waypoint number = 50 and population size = 100), the SDCS method achieved a 0.983% improvement over the result obtained with the FFO algorithm. In Case 3 (waypoint number = 100 and population size = 50), the SDCS method outperformed the FFO algorithm by 0.538%. In Case 4 (waypoint number = 100 and population size = 100), the SDCS method achieved a 0.538% improvement over the result obtained with the FFO algorithm as well. Considering Tables 10 and 11, increasing the population size in MH algorithms increased the chance of obtaining better results. Tables 10 and 11 show that the results obtained with MH algorithms may change with the change in population size. Thus, SDCS should be utilized in obtaining optimal solutions in min-max TSP with multiple depots for multi quadcopters. In the literature, there are several studies that include the control of the quadcopter. A previous study focused on the comparison of the performances of the classical PID controller and the FOPID controller, in full control of the quadcopter. The results of the study proved the superiority of the FOPID controller over the classical PID controller [8]. The altitude control of the quadcopter was performed using the SDCS controller. The performance of the resulting controller was compared with the classical PID controller method, where the parameters of the controller were calculated using the Dragonfly algorithm [32]. This study made its contribution to the literature by comparing the performances of these two efficient methods in full control of the quadcopter. In this study, to validate the success of SDCS, the control of the quadcopter was realized with FOPID controllers.