## 5 Results and Discussion
### Stability Analysis  
In addition to the analysis of the results and average data of the three scenarios, the stability and distribution of the results are also statistically analyzed. This subsection compares and analyzes the running time and the resulting stability of three algorithms. Figure 11 is a comparison of the running time distribution of the PSO, FFA and SHH algorithm in term of running 200 times in scenario 3. The left graph is the statistical result of the PSO. It can be seen that the running time of PSO was concentrated between 2.48 s and 2.35 s, and the median value was approximately 2.39 s. There were many abnormal points on the upper side of the concentrated distribution range, and the largest abnormal point reached approximately 2.62 s. The middle graph is the statistical result of the FFA. Its running time was concentrated between 0.58 s and 1.2 s, and the median was approximately 0.75 s. There were more abnormal points on the upper side of the concentrated distribution range, and the largest abnormal point reached approximately 1.80 s. The right graph is the statistical result of the SHH. It can be seen that the running time of the SHH was concentrated between 0.61 s and 0.65 s, and the median value was approximately 0.62 s. A small number of abnormalities appeared on both sides of the concentrated distribution range. More specifically, the largest abnormal point was approximately 0.69 s, and the smallest abnormal point was approximately 0.60 s. Through comparing the running time distribution from Figure 11, the SHH algorithm that has the most stable and most concentrated distribution of running time among the three algorithms can be obtained. The results indicated the SHH algorithm had a good calculation time stability performance in solving the proposed path planning model in this paper. The time efficiency of the online learning SHH algorithm improved at least 20% compared with those of PSO and FFA. Figure 12 is a statistical comparison of the distribution of the final cost values of PSO, FFA, and SHH algorithms after running 200 times in the case of scenario 3. The left graph is the running result of the PSO. It can be seen that the optimal cost was mainly concentrated in the range of 1027 to 1101, and the median value was approximately 1030. There were some abnormal points on the upper side of the concentration range, and the maximum value of these abnormal points was approximately \begin{table}
\begin{tabular}{c c c c} \hline  & **PSO** & **FFA** & **SHH** \\ \hline Computing time & 2.4010s & 0.8233s & 0.6241s \\ Final total cost & 1072.8236 & 935.7844 & 893.2590 \\ Time cost value & 185.5981 & 188.2685 & 187.1679 \\ Map cost value & 0 & 0 & 0 \\ Curvature cost value & 408.0219 & 232.7617 & 240.2193 \\ Ocean current cost value & 479.2037 & 514.7541 & 465.8717 \\ \hline \end{tabular}
\end{table} 
Table 4: Results comparison using PSO, FFA and SHH algorithms in Scenario 3. Figure 11: Comparison of the stability of running time of PSO, FFA and SHH algorithms. 1195. The middle graph was the running result of the FFA. It can be seen that the optimal cost after optimization was mainly concentrated between 910 and 965, the median was around 935. A small number of abnormal points appeared on the upper side of the concentration range. The maximum was approximately 995. The right graph is the running result of the SHH. It can be seen that the optimal cost value after optimization was mainly concentrated between 860 and 930, the median was around 890. A small amount appears on both the upper side and the lower side of the concentration range. Among the abnormal points, the smallest abnormal point was near 840, and the largest abnormal point was near 980. By comparing the three cost stability analysis graphs, it can be obtained that the cost value distribution of the SHH and FFA have the consistent ranges, that is, the final cost stability of the two algorithms is equivalent.