# Multi-UAV Mission Allocation under Constraint
## 4 Simulation Results  
In this section, three simulation experiments to illustrate the method of Lingo software and the improved model are presented. The simulation results show the advantages of the new model with respect to the strategic intention expression and resource utilization scenarios. For each simulation experiment, we performed 10 simulations separately and selected the best data for display. All simulation experiments are performed on an computer with Intel(r) Core(tm) i5 CPU, 2.3 GHz, 4.00 GB of RAM (Intel Corporation, Santa Clara, CA, USA). In the combat mission scenario, there are five UAVs with the same properties, where the flight speed is 260 km/h, and the scan width of reconnaissance sensor is 0.3 km. The maximum flight time of the UAV is 18 h and the maximum opening time of the reconnaissance sensor is 6 h. There are 25 target areas to be reconnoitered and randomly distributed in an area of \(1000\times 1000\) square kilometers. Specific target area parameters are shown in Appendix A Table A2. Appendix A Table A3 shows the reconnaissance time allocation for each target area in the optimization scheme and the corresponding reconnaissance reward and reconnaissance ratio obtained. The minimum scouting rate is limited in the reconnaissance area with a number of 60%. It can be found that a large proportion of the value coefficient affects the reconnaissance ratio of the mission. The bar chart of the reconnaissance time allocation is shown in Figure 3. A bar chart comparing the value coefficient of each reconnaissance area with the reconnaissance reward is shown in Figure 4.  
Table 1 shows assignment status of each UAV. Each UAV is assigned five reconnaissance areas with a total reconnaissance duration of 6 h. Due to the time window, UAV1 flies to the reconnaissance target 23, and it waits for 1.5342 h to start reconnaissance. UAV4 flies to the reconnaissance target 25, and it waits for 0.665 h to start reconnaissance. The total time of the entire operational plan is the  
Figure 4: Value coefficient and reconnaissance reward of each target area in the scenario. Figure 3: Reconnaissance time of each target area in the section. time when the last UAV returned to the base, for which the flight time of UAV2 is 15.6689 h. The total reconnaissance reward is 12.4338, while the total flight duration is 72.5758 h. It can be seen from Table 1 that even with the distribution of reconnaissance tasks under the time window, the method we use can ensure that multiple UAVs allocate time as evenly as possible. The total flight of each drone is controlled within 14 to 16 h, and can be found in conjunction with Figure 5, the flight path of the drone is also reasonable and efficient. The flight path of the final plan is shown in Figure 5.  
The whole simulation process takes 4.3 s. The same model is simulated by genetic algorithm (GA) and non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The central idea of the genetic algorithm is to select, cross, and mutate the genetic population composed of a certain number of individuals, and finally, obtain the optimal solution or approximate optimal solution. When performing genetic operations, several important parameters are as follow: chromosome length L, population size M, crossover probability Pc, mutation probability Pm, and termination generation G. In this simulation experiment, Pc = 0.4 and Pm = 0.1. The basic operation process of the genetic algorithm is as follow:  
(a) Initialization: Set the evolution algebra counter gen = 0, set the maximum evolution algebra G, and randomly generate M individuals as the initial population P(0).