## 5 Results
### Comparison of Different Algorithms for Path Planning
#### 5.2.2 Path Planning in the Urban Environment  
Figures 9 and 10 show the results of 1 representative experiment selected from 12 experiments comparing the performance of the six algorithms for UAV flight paths in urban environments. Figure 11 gives the graphs of the fitness of the six algorithms in two different urban environments with the number of iterations.  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithmic** & **Parameter** & **Value** \\ \hline \multirow{5}{*}{SAVOA} & Warning parameter \(ST\) & 0.8 \\  & Percentage of discoverers \(p_{d}\) & 0.2 \\  & Percentage of vigilantes \(p_{v}\) & 0.15 \\  & Control parameter \(P_{2}\) & 0.4 \\  & Control parameter \(P_{3}\) & 0.6 \\  & Control parameter \(w\) & 2.5 \\ \hline \multirow{5}{*}{AVOA [39]} & Control parameter \(P_{1}\) & 0.6 \\  & Control parameter \(P_{2}\) & 0.4 \\  & Control parameter \(P_{3}\) & 0.6 \\  & Control parameter \(w\) & 2.5 \\ \hline \multirow{2}{*}{SAA [40]} & Warning parameter \(ST\) & 0.8 \\  & Percentage of discoverers \(P_{d}\) & 0.2 \\  & Percentage of vigilantes \(P_{v}\) & 0.15 \\ \hline \multirow{2}{*}{ABC [41]} & Number of honeys harvesting bees \(N_{e}\) & 0.2 \(\times\)\(pop\) \\  & Judgment threshold \(limit\) & 5 \\ \hline \multirow{2}{*}{WOA [42]} & Random number between 0 and 1 & \(r\) \\  & Convergence factor a & \(a=2-2\cdot iter/iter_{Max}\) \\  & Control parameter \(A\) & \(A=2a\cdot r-a\) \\ \hline \multirow{2}{*}{GA [43]} & Probability of selection \(P_{s}\) & 0.5 \\  & Crossover probability \(P_{c}\) & 0.8 \\ \cline{1-1}  & Probability of mutation \(P_{m}\) & 0.2 \\ \hline \end{tabular}
\end{table}
Table 4: Algorithmic parameters.  
Figure 8: Comparison of fitness-function convergence curves in mountainous environments.  
Figure 6: Top-view comparison of drone path planning in mountainous environments. **(a,b)** represent the top-view maps with 20 and 25 mountain peaks, respectively. The red arrow indicates the direction of the wind field.  
Figure 7: Stereogram comparison for UAV path planning in mountainous environments. **(a,b)** represent the three-dimensional views with 20 and 25 mountain peaks, respectively. The red arrow indicates the direction of the wind field.  
All the above images are representative results from 1 of the 12 experiments. The results show that the SAVOA outperforms AVOA, SSA, WOA, GA, and ABC algorithms in the same manner as the other ones in the previous experiments.  
Figure 11: Comparison of fitness-function convergence curves in urban environments.  
Figure 10: Comparison of stereograms for UAV path planning in urban environments. (**a**,**b**) are stereograms for building numbers 20 and 25, respectively. The red arrow indicates the direction of the wind field.  
Figure 9: Comparison of top views for UAV path planning in urban environments. (**a**,**b**) are top views with 20 and 25 buildings, respectively. The red arrow indicates the direction of the wind field.  
both mountainous and urban environments and is able to plan paths that are close to the shortest distance between the start and end points. Meanwhile, according to the iteration graph, the optimal fitness of SAVOA is also at the lowest level and has the best convergence performance. Since the fitness values in this paper are based on weighted results, there is no fixed theoretical optimum, but the shortest path is fixed and can have a reference value: \(F_{t\_theoretical}=l_{1}f_{1}=440.203\).