# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 5 Analysis
### Comparison of Algorithm Performance  
To further verify the exploration ability and optimality of the GWO algorithm, the results were compared with those given by particle swarm optimization (PSO). PSO is a swarm heuristic algorithm that simulates the flocking behavior of birds and searches for an optimal solution through collaboration and information sharing among particles in the flock [28]. The convergence speeds of the GWO and PSO algorithms are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Selected** \\ **Points** \\ \end{tabular} & \
\begin{tabular}{c} **Type of** \\ **Co-Distribution** \\ **Center** \\ \end{tabular} &
\begin{tabular}{c} **Corresponding Set of Demand Points** \\ **Corresponding Set of Demand Points** \\ \end{tabular} \\ \hline
27 & UAV & 22, 25, 26, 33, 34, 35, 36, 38, 51, 59, 64, 66, 68, 69, 90, 92 \\ \hline
28 & Vehicle & 1, 2, 4, 5, 6, 7, 9, 10, 11, 13, 15, 18, 20, 21, 27, 29, 30, 31, 42, 50, 87 \\ \hline
29 & Vehicle & 105, 110, 112, 139, 144, 155, 160, 168, 185, 200, 202, 215, 224, 242, 247, 303 \\ \hline
30 & Vehicle & 3, 8, 16, 17, 19, 23, 44, 46, 47, 48, 52, 53, 54, 56, 73, 80, 81, 82, 96 \\ \hline
31 & UAV & 361, 362, 363, 381, 383, 384, 386, 387, 390, 392, 393, 407, 414, 416, 417, 420, 421, 444, 447, 448, \\ \hline
33 & Vehicle & 49, 57, 58, 60, 61, 74, 77, 79, 84, 85, 100 \\ \hline
36 & Vehicle & 158, 181, 208, 211, 240, 244, 276, 277, 278, 306, 307, 332, 333, 344, 345, 366 \\ \hline
38 & UAV & 385, 389, 403, 404, 412, 424, 426, 436, 437, 439, 441, 461, 462, 471, 474, 476, 480, 483, 485, 487 \\ \hline
43 & Vehicle & 97, 123, 124, 143, 150, 151, 152, 176, 177, 178, 182, 193, 194, 195, 227, 228, 232, 282, 284, 314 \\ \hline
47 & UAV & 197, 245, 275, 283, 305, 312, 326, 330, 337, 338, 350, 356, 357, 359, 372 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 6: Results of site selection for the co-distribution centers.  
The global information-sharing mechanism of PSO ensures a rapid initial convergence speed, but after a certain number of iterations, the algorithm becomes stuck around a locally optimal solution and its fitness remains unchanged. The social hierarchical structure and the group-hunting mechanism of GWO ensure a strong exploration ability, allowing it to continue moving toward a globally optimal solution. The adaptability of GWO decreases as the number of iterations increases, until adaptability stabilizes. The fitness of the optimal solution is improved by about 34.1% compared with that of PSO. The figure also shows that the gray wolf algorithm performs better than the particle swarm algorithm in terms of global exploration ability. By dynamically adjusting the position of the leader wolf and the position of other individuals, it can effectively balance exploration and exploitation and reduce the possibility of premature convergence.  
In its implementation, the gray wolf algorithm needs to set only the size of the population and the number of iterations, without the need for a large number of parameters to be set and tuned, and the implementation is relatively simple. However, the particle swarm algorithm needs to adjust a number of parameters, such as inertia weights, learning factors, and speed range, and its operation speed and convergence speed are greatly affected by the impact of a single parameter; in addition, it is more unstable, which affects the overall efficiency of the algorithm.