# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 2 Path Planning Algorithm
### Improvement of the A* Algorithm
#### 2.2.2 Optimization Using the SSA
The simulation experiments are conducted in two types of map environments: high-density clustered maps and low-density random maps. Each environment is tested on maps of three different sizes: 20 \(\times\) 20, 35 \(\times\) 35, and 50 \(\times\) 50. For a given environment and map size, we first apply the SSA-A* to optimize the weight parameters and obtain the heuristic function \(h(n)\). Then, we conduct path planning tests on 15 maps of the same environment and size. The paths generated by the SSA-A* are compared to those produced by Dijkstra's Algorithm, the Classical A* Algorithm, the JPS-A* Algorithm (an improved version of A* algorithm), and ACO. The parameters used in the SSA optimization process are listed in Table 1.

#### 4.1.2 Results and Analysis of Path Planning Simulation Experiment

Figure 4 shows the path planning simulation experimental results for a high-density clustered map on a 35 \(\times\) 35 grid map. (a) corresponds to the path generated by the SSA-A*. (b) corresponds to the path generated by the Classical A* Algorithm. (c) corresponds to the path generated by [PERSON]'s Algorithm. (d) corresponds to the path generated by the JPS-A* Algorithm. (e) corresponds to the path generated by ACO. Figure 5 presents the path planning simulation experimental results for a low-density random map on a 35 \(\times\) 35 grid map. (a) corresponds to the path generated by the SSA-A*. (b) corresponds to the path generated by the Classical A* Algorithm. (c) corresponds to the path generated by the JPS-A* Algorithm. (d) corresponds to the path generated by the \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value or Range** \\ \hline \(\omega_{1}\) & [0.5, 2] \\ \(\omega_{2}\) & [0.5, 2] \\ \(\omega_{3}\) & [0.1, 1] \\ Population Size (n) & 10 \\ Number of Iterations & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for path planning simulation experiments. Figure 4: Simulation experimental results of five path planning algorithms on a high-density clustered map. (**a**) is the SSA-A*; (**b**) is the Classical A* Algorithm; (**c**) is Dijkstra’s Algorithm; (**d**) is the JPS-A* Algorithm; (**e**) is ACO. Figure 5: Simulation experimental results of five path planning algorithms on a low-density random map. (**a**) is the SSA-A*; (**b**) is the Classical A* Algorithm; (**c**) is [PERSON]’s Algorithm; (**d**) is the JPS-A* Algorithm; (**e**) is ACO. Figure 6 shows the comparison of the running time between the SSA-A* and the Classical A* Algorithm. The running times of both algorithms are normalized with respect to the running time of [PERSON]'s Algorithm ([PERSON] = 1). A lower value indicates a shorter running time. Figure 7 presents a comparison of the search node count and path length between the SSA-A* and Classical A* Algorithms. Search Efficiency (Left Axis): The number of search nodes for both the SSA-A* and Classical A* algorithms is normalized with respect to the number of search nodes in [PERSON]'s Algorithm ([PERSON] = 1). A lower value indicates higher search efficiency. Path Quality (Right Axis): The SSA-A* path length increase (relative to the shortest path from Dijkstra) is shown as a percentage.