## 5 Simulation of Unmanned Delivery Vehicle Path Planning Based on the MSEChoA Algorithm
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & **Algorithm Name** & **GWO** & **EO** & **ALO** & **MSGWO** & **ChoA** & **ICHOA** & **WChOA** & **AWChOA** & **MSECHOA** \\ \hline \multirow{4}{*}{Simple} & Average Path & 49.799 & 48.0416 & 48.0416 & 48.0416 & 55.0711 & 47.3137 & 48.2132 & 54.4853 & 45.6985 \\  & Length & & & & & & & & \\ Map & Average Standard & & & & & & & & \\  & Deviation & 0.6679 & 1.3099 & 0.96255 & 4.5299 & 2.6 & 1.2083 & 1.0479 & 1.5778 & 1.1419 \\  & Average Runtime (s) & 7.9166 & 7.8611 & 11.8017 & 9.9254 & 7.7053 & 7.7646 & 5.5806 & 8.1135 & 5.2658 \\  & Average Journey & & & & & & & & \\ Complex & Length & & & & & & & & \\ Map & Average Standard & & & & & & & & \\  & Deviation & 0.26197 & 1.0664 & 1.4163 & 2.8211 & 1.6867 & 2.1569 & 0.95768 & 1.8779 & 0.52394 \\  & Average Runtime (s) & 9.9677 & 9.8957 & 11.8057 & 8.9797 & 8.8052 & 10.5313 & 7.9110 & 8.1135 & 7.6658 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Path Planning Results of Various Algorithms on Two Different Maps. Figure 7: Path planning results of various algorithms in simple and complex environments. (**A**,**B**) Path comparison of MSECHOA with other intelligent optimization algorithms in a simple map; (**C**,**D**) path comparison of MSECHOA with other intelligent optimization algorithms in a complex map. ChOA (54.4853), demonstrating MSEChoA's significant advantage in handling complex path planning tasks. Regarding path planning stability, the MSEChoA algorithm has an average standard deviation of 1.1419 in a simple environment, slightly higher than WChOA's 1.0479, but still at a low level. In a complex environment, MSEChoA's average standard deviation further decreases to 0.52394, the lowest among all algorithms, indicating that the algorithm can not only find shorter paths in complex environments, but also has high path stability and robustness. Additionally, in terms of runtime, MSEChoA also demonstrates high computational efficiency in path planning tasks. An analysis of Table 6 shows that the MSEChoA algorithm performs excellently in both simple and complex environments, with its performance in complex environments further validating its potential as an efficient and reliable solution for unmanned delivery vehicle path planning. Meanwhile, in order to simulate a more realistic delivery scenario, this study also simulates a warehouse environment and merges two randomized tasks, where the start point and end point are randomly generated (triangles represent the start point, circles represent the end point, and the blue lines are the routes planned and traveled by the unmanned vehicle) as well as dynamic obstacles. The yellow squares are dynamic obstacles and the dotted line behind them is the trajectory of the dynamic obstacles. The gray squares are unknown obstacles. Dynamic obstacles change their positions through random movements, further increasing the complexity of the scenario and the adaptability requirements of the algorithm, as shown in Figure 8:  
The experimental results show that in tasks simulating real delivery scenarios, the MSEChoA algorithm can quickly generate optimal paths that avoid dynamic obstacles. Moreover, its stability is evident in complex scenarios involving random tasks and dy  
Figure 8: Simulated real delivery scenarios: (**A**,**B**) represent two random tasks. namic obstacles, demonstrating superior global search and local exploitation capabilities compared to other algorithms. This further validates the potential of the MSECHOA algorithm in practical applications, particularly in dynamic logistics and unmanned vehicle delivery domains.