## 5 Experiment
### Comparison Experiment of Intelligent Path Planning Algorithms
#### 5.4.2 Obstacle Rate  
When there are many obstacles, the intelligent optimization algorithm may fail to find a feasible solution. Therefore, we conducted comparative experiments on the success rate and solution quality of each algorithm under three different obstacle rates. The success  
Figure 19: Comparison experiment of intelligent path planning algorithms (search time).  
Figure 18: Comparison experiment of intelligent path planning algorithms (path length).  
of an algorithm is marked by successful population initialization and finding a feasible solution within the specified number of iterations. The experiment involved randomly generating three sets of grid maps, with 50 maps in each set. Each map has a side length of 500 \(\times\) 500, and the obstacle rate \(\rho\) was set to 0.1, 0.2, and 0.3, respectively. Obstacle grids and start/finish points were randomly generated on each map (ensuring there is a feasible solution). The parameter settings for each algorithm are the same as in Section 5.4.1. The success rate test results for each algorithm are shown in Table 2. In cases where the algorithms succeeded, the comparison of path lengths, search times, and SGPVEFA is presented in Table 3.  
As shown in Table 2, the proposed algorithm can still accurately find feasible solutions for complex maps. However, as the obstacle rate increases, the success rate of the intelligent optimization algorithm drops significantly. When the obstacle rate reaches 0.3, the success rate is below 10%, and for Genetic Algorithm (GA), it is only 2%, making it almost impossible to find a solution. This highlights the superiority of SGPVEFA in high-obstacle environments. In Table 3, as the obstacle rate increases, the search time and path length of each algorithm decrease. This is because the starting and ending points are randomly generated, and the intelligent optimization algorithm can only handle short-distance problems under high obstacle conditions. For longer distances between the starting and ending points, no feasible solution can be found. In short-distance scenarios, both the path length and search time are reduced.  
It can be seen that in the three obstacle rate environments, SGPVEFA's search time and path length are better than those of intelligent optimization algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Algorithm** & **SGPVEFA** & **GA** & **GWO** & **DE** & **PSO** & **SSA** \\ \hline Obstacle rate & Number of runs & 50 & 50 & 50 & 50 & 50 & 50 \\ \hline \multirow{2}{*}{0.1} & success & 50 & 14 & 32 & 32 & 32 & 31 \\ \cline{2-8}  & success rate & **100\%** & 28\% & 64\% & 64\% & 64\% & 62\% \\ \hline \multirow{2}{*}{0.2} & success & 50 & 7 & 19 & 19 & 19 & 19 \\ \cline{2-8}  & success rate & **100\%** & 14\% & 38\% & 38\% & 38\% & 38\% \\ \hline \multirow{2}{*}{0.3} & success & 50 & 1 & 4 & 4 & 4 & 4 \\ \cline{2-8}  & success rate & **100\%** & 2\% & 8\% & 8\% & 8\% & 8\% \\ \hline \end{tabular}
\end{table}
Table 2: Success rate of each algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Obstacle Rate** & **Algorithm** & **SGPVEFA** & **GA** & **GWO** & **DE** & **PSO** & **SSA** \\ \hline \multirow{2}{*}{0.1} & path length & **161.41** & 644.6 & 654.27 & 929.55 & 791.47 & 281.26 \\ \cline{2-8}  & search time & **4.06** & 6.84 & 13.85 & 13.75 & 13.49 & 13.74 \\ \hline \multirow{2}{*}{0.2} & path length & **185.4** & 485.63 & 460.11 & 584.79 & 541.41 & 257.25 \\ \cline{2-8}  & search time & **4.62** & 26.45 & 25.64 & 25.04 & 27 & 27.31 \\ \hline \multirow{2}{*}{0.3} & path length & **93.26** & 186.4 & 124.99 & 168.09 & 119.89 & 102.09 \\ \cline{2-8}  & search time & **0.78** & 4.79 & 2.12 & 1.86 & 1.53 & 1.77 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of path length and search time with GA.