## 4 Conclusions  
To overcome the problems of long computational time consumption, severe performance degradation, and poor compatibility of traditional maritime route planning in terms of long-distance planning tasks, multiple constraints, and high-resolution data nowadays. In this paper, a robust, high-performance and computationally efficient parallel route planning algorithm, namely SPA*, is proposed. The theory of parallel computing is applied in SPA*. SPA * is suitable for solving the minimum risk path problem, and risk values are obtained by evaluating predictive data for dynamic, uncertain marine environments. Through static planning experiments with three map sizes and dynamic planning experiments with two sizes of forecast data in Indonesian waters, we obtain the following conclusions:  
1. The performance and computational efficiency of SPA* is excellent. In static experiments, the comparison subjects are traditional path planning algorithms (Dijkstra, A*, and BA*) and SI-based path planning algorithms (ACO, HHO, and SSA). The time of SPA* is reduced by about 5-12,425 times compared with each algorithm, and the R-VIMO of it is always kept at 0. Meanwhile, in the control of \(f_{1}\) and \(f_{2}\), the performance of SPA* is improved by 7.68%-25.14% and 8.44%-14.38% compared with each algorithm. In dynamic experiments, SPA* is compared with traditional path planning algorithms (Dijkstra, A*, and BA*) and SI-based path planning algorithms (SSA). The time of SPA* is reduced by about 4.8-1262.9 times compared to each algorithm, and the R-VIMO of it is always kept at 0. In the control of \(f_{1}\) and \(f_{2}\), the performance of SPA* is improved by 3.87%-9.47% and 7.21%-10.36%, respectively, compared with each algorithm. 2. SPA* has the advantage of fewer parameters and strong robustness. In the experiments for the only parameter \"the number of segment \(m\)\", it is adjusted substantially, but the time of SPA* is changed to a small range. For 100 \(\times\) 100, 200 \(\times\) 200, and 400 \(\times\) 400, the change in running time is about 0.01 s, 0.04 s, and 1.8 s, respectively. For the control of risk indicators \(f_{1}\) and \(f_{2}\), we analytically give some reasonable intervals of \(m\) for SPA* at different map sizes. For 100 \(\times\) 100, the lower \(m\) the better, but not less than 6; for 200 \(\times\) 200, \(m\) should be 10-20; for 400 \(\times\) 400, \(m\) should be 15-30. After limiting the range of \(m\), SPA* shows great control at \(f_{1}\) and \(f_{2}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multirow{2}{*}{**Our Algorithm**} & \multicolumn{2}{c}{**Traditional Path Planning**} & \multicolumn{2}{c}{**SI-Based Path**} & \multirow{2}{*}{**Forecast Data**} \\  & & & & & & \\ \cline{2-2} \cline{5-6}  & **SPA* & **Dijkstra** & **A* & **BA* & **SSA** & \\ \hline Time(s) & 9.3600 & 904.8657 & 269.0035 & 84.8583 & 45.3768 & \\ \(f_{1}\) & 0.1166 & 0.1228 & 0.1238 & 0.1213 & 0.1082 & Daily \\ \(f_{2}\) & 0.2137 & 0.2384 & 0.2374 & 0.2432 & 0.2420 & (Total 7 days) \\ R-VIMO & 0 & 0 & 0 & 16.78\% & 31.60\% & \\ \hline Time(s) & 2.4335 & 3073.3542 & 937.1178 & 327.9520 & 321.9712 & Every 6 h \\ \(f_{1}\) & 0.1137 & 0.1256 & 0.1254 & 0.1235 & 0.1084 & (total 28 periods) \\ R-VIMO & 0 & 0 & 0 & 22.87\% & 30.03\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dynamic planning results for SPA*, traditional path planning, and SI-based path planning with different data scales. When the planning scenario satisfies long-distance planning tasks, multi-constraint conditions, and high-resolution data requirements, we note that our algorithm outperforms the swarm intelligence algorithm. This is likely because the traditional metaheuristic procedures to solve the path planning problem step is: First search for the key points of a path, then connect the key points to form a path, and finally iteratively obtain the planning path. Then, the connection algorithm determines, to a large extent, the sensitivity of the planning path to grid data.