## 5 Experimental Simulation Analysis
### Analysis of Results
It is 86.35%, which is 86.43% shorter than the VAINDIWPSO algorithm. The running velocity and efficiency of the algorithm are improved, and it can rapidly and precisely bounce out of the local optimum, attain the global optimum solution, and reap the optimum path. Figure 8 shows that when the algorithm converges, the fitness function value will continue to be unchanged. Moreover, it is acknowledged from the above that the IC-VAINDIWPSO algorithm does not easily fall into the local optimum, so according to Figure 8, the algorithm stops running after 50 iterations after it converges. This technique can attain the optimal fitness value with a small number of iterations, which considerably reduces the running time of the algorithm, and permits the UAV to rapidly and precisely design near-optimal solutions. Figures 9-12 exhibit the 3D path planning diagrams and side views of the paths acquired by using the PSO algorithm and the IC-VAINDIWPSO algorithm in complicated terrain. It can be seen that the path acquired via the PSO algorithm is relatively bad and not smooth. The IC-VAINDIWPSO algorithm can rapidly discover the near-optimal solutions and keep a suitable flight height with the terrain, and the path is additionally smooth and effective. According to the data in Table 5, we choose different numbers of threats according to the serial numbers of the threats to acquire different environmental models, and then we use the above three algorithms to conduct 30 simulation experiments in different environments to acquire the average value of the fitness function. As shown in Figure 13, when the number of threats is less than three, the three algorithms can acquire the optimal solution, and the average value of the acquired fitness function is now not very different. However, as the environment will become extra complex, the PSO algorithm and the VAINDIWPSO algorithm will fall into a local optimum, and the IC-VAINDIWPSO algorithm overcomes this shortcoming. So the gap between the average values of the fitness functions of the three algorithms will grow larger, and the degree of pros and cons of the acquired path will also be different. It can be seen from Table 6 that when the number of threats is small, the influence of threats on the acquired path is relatively small. The VAINDIWPSO algorithm and the IC-VAINDIWPSO algorithm can directly acquire near-optimal solutions, whilst the PSO algorithm still needs 153 iterations to converge, its average fitness function is the lowest, and the initialization time is additionally the shortest. As the number of threats progressively increases, the benefits of the IC-VAINDIWPSO algorithm steadily emerge. It can be seen from Table 7 that the iteration convergence times of the IC-VAINDIWPSO algorithm are 25 times, the average fitness function is 5229.1847, and the initialization time is 0.629 s, which is 8.2% shorter than the VAINDIWPSO algorithm and 44.53% shorter than the PSO algorithm. Compared with Table 5, it can be seen that the more complex the environment is, the more obvious the optimization effect of the IC-VAINDIWPSO algorithm is, and the greater the gap between the three algorithms is. Figures 14-17 are the top views of the PSO algorithm and the IC-VAINDIWPSO algorithm in three threats and five threat era paths. It can be seen that the IC-VAINDIWPSO algorithm acquires better flyable paths. Figure 12: The 3D path side view of the IC-VAINDIWPSO algorithm. Figure 13: Average of fitness functions for different numbers of threats. Figure 14: The top view of the paths generated by the PSO algorithm under three threats.