## 4 Experiment and Result Analysis
### Comparison with Centralized Global Optimization Based on GA  
In the proposed distributed framework, each UAV only focuses on the nearby UAVs and tasks, and negotiates with the nearby UAVs to resolve the conflicts in the combat plans. This distributed solution not only realizes decoupling between UAVs, but also effectively reduces the search space of the task-allocation problem, and can improve the speed of solving the problem. Based on the scenario shown in Figure 3, we compare the proposed method with the centralized global optimization method based on GA in terms of solving speed and quality. The experimental platform is a MSI GS65 notebook installed with Windows system. Its CPU is i7-8750H, GPU is GTX1070 Max-Q, memory is 32 G, and the SSD is 512 G.  
We have counted the decision-making time consumption of each reconnaissance UAV in steps 1-8 during the optimization of reconnaissance task allocation, because all UAVs have confirmed their tasks at the end of the 8 th simulation step. The results are shown in Figure 7, and it can be found that the time consumed by each decision of each UAV is less than 0.25 s. Without considering the communication delay, if the maximum decision time of each step is taken as the cycle of this round of iteration, the total time of 8 simulation steps is 1.02 s. From the time-consumption distribution, it can also be found that the first step of decision-making only consumes a little time, while the second step of decision-making  
Figure 6: Gantt chart for reconnaissance and strike tasks. Blue boxes represent reconnaissance and red boxes represent strikes, and the triple elements represent mission confirmation time, duration of maneuver and task execution, and the concatenation of task type and task ID respectively. takes the most time, and then decreases gradually. This is because before the first decision, there is no communication between nodes, and each node makes decisions independently. The second decision is made after exchanging the results of the first round, and at this time, each node is performing conflict resolution on multiple collected plans, so it takes a lot of time. After the second step, the conflict between plans is gradually resolved, so the decision-making time is also shortened and the final task-allocation plan is formed. The optimization results are shown in Table 6, and from the global perspective, its fitness is 27.48 using Equation (9). In the same scenario, we further use the centralized GA to optimize the reconnaissance task-allocation problem from a global perspective, and the fitness curve obtained is shown as the two CGA curves in Figure 8. In our proposed algorithm, the population number \(\pi_{n}\) and iteration number \(\pi_{i}\) of GA are automatically adjusted according to the number of permutations of the assignment problem. When this strategy is applied to the centralized method, the obtained parameters of GA are \(\pi_{n}=32\), \(\pi_{i}=30\) and \(\pi_{p}=0.1\). However, the search space for the global optimization problem of assigning 30 tasks to 13 UAVs is too large, so GA is difficult to converge to a good result under these parameters. In order to further expand the search of the global GA to obtain a better result, we adjust the parameters to \(\pi_{n}=400\), \(\pi_{i}=200\), \(\pi_{p}=0.3\). We can find that after 27 rounds of iteration, it has obtained a result with fitness close to that of the proposed paper, which consumes about 4.9 s.  
Comparing the time consumption of the two methods, it can be found that the proposed method effectively reduces the computing load of each single UAV through the idea  
Figure 8: The fitness and time consumption of centralized GA.  
Figure 7: Time consumption of each UAV in the proposed method. of divide and conquer, and can be applied to more types of small UAVs. By observing the change of fitness, we found that it is easy for centralized global optimization to fall into local optimal solution when the problem space is large.