# A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning
## 5 Application of Mobile Robot Path Planning Based on FMMPA
### Simulation Experiments and Analysis  
The simulation experiment was selected with a population size of 30 and a maximum number of iterations of 500. To verify the optimal performance of the FMMPA algorithm in solving the global path planning problem under different static environments, three normal grid environments of 10 \(\times\) 10, 30 \(\times\) 30 and 50 \(\times\) 50 were selected and tested 20 times. Experimental verification of the performance improvement effect of FMMPA with the original MPA algorithm and the other five algorithms in terms of path length and finding efficiency. Table 8 shows the parameters of the normal experimental environment. According to Figure 7a, FMMPA can successfully find the shortest path in a simpler map 1, while SSA cannot successfully avoid obstacles and fail in path planning. Only the path of EO is different from the other algorithms, and the paths of the SCA, GWO and WOA completely overlap with the MPA so that they are covered by the MPA curve According to Figure 7b, SSA has the worst search path length. The path length curves of SCA, GWO, WOA and EO almost coincide with the MPA curves. The number of iterations for WOA is slightly higher than the other algorithms. The FMMPA path lengths are decrease in a stepwise manner, indicating that the algorithm is constantly jumping out of the local optimum to the shortest path value during the iteration process. The algorithm can converge quickly in the initial phase optimization, verifying the effectiveness of the [PERSON] spiral search strategy to avoid blind search and demonstrating the effectiveness of the nonlinear convex weight strategy to enhance the global exploration capability. In the intermediate and late-phase optimization, the convergence curve is continuously decreasing, indicating that the local exploitation ability of the algorithm is continuously enhanced, and suggesting that the effectiveness of the Gold-SA strategy to accelerate the convergence speed and constantly improve the convergence accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **Grid Specification** & **Number of Obstacles** & **Obstacle Percentage** \\ \hline Map 1 & \(10\times 10\) & 31 & 31\% \\ \hline Map 2 & \(30\times 30\) & 117 & 13\% \\ \hline Map 3 & \(50\times 50\) & 267 & 10.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Normal experimental environment parameters. Figure 7: Experimental results for map 1. In Figure 7(b), the FMMPA algorithm achieves better search accuracy than the original MPA in the first iteration, and finally achieves the optimal solution better than all other algorithms, verifying the effectiveness of [PERSON] spiral search path strategy in expanding the global search domain and improving the search accuracy in the initial iteration. In Figures 7(a) and 7(a), in both the different size normal environment map 2 and map 3, FMMPA can find the shortest paths that outperform the rest of the algorithms, and according to Figure 7(b), the algorithm convergence curve is flatter, but the search for the best value is still the best. In Table 9, the average path value of FMMPA is the shortest length in all three different environments, and the standard deviation of path length demonstrates significant improvement compared to the other algorithms. To further verify FMMPA's ability to solve the local optimum problem, experiments were conducted in two separate environments of greater complexity. The environment not only had a much higher percentage of obstacles than the normal environment but also had a higher complexity of obstacle shapes. According to Figure 9(a), both the original MPA and the FMMPA can successfully plan the path and obtain the optimal path in the complex environment of map 4, while the other algorithms cannot successfully plan the route, highlighting the superior performance of the original MPA algorithm. It is also seen that the path planned by FMMPA is better than MPA.