# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
## 4 Analysis of Examples
### Algorithm Analysis  
The algorithm was programmed and experimented with using MATLAB2020B. The experimental computer environment was an Intel 11 th Gen Core [EMAIL_ADDRESS] GHz quad-core with 16 G of RAM. Sensitivity analysis of IIA was performed, and it was found that the population size affects the search range of the algorithm. Theoretically, increasing the number of iterations increases the likelihood that the algorithm will find a globally optimal solution. In our experiments, we took 50 times, that is 300 times, as the maximum number of iterations after the solution results were stabilized and there were no more changes. The crossover probability and variance probability are used to balance the local optimal solution and the global optimal solution, so the crossover probability is taken as 0.5 for stabilizing the speed and aggregation of the search process. The variance probability is taken as 0.4 to increase the population's diversity and not make the search process too scattered and difficult to converge. The diversity evaluation parameter prevents the solutions from being too similar and falling into local optimal solutions, and 0.95 can effectively ensure the diversity of the solution results. The population size of 200 is determined mainly based on the number of candidate points. This not only helps to improve the search space of the algorithm but also ensures its performance while considering computational efficiency. The memory capacity is the maximum capacity to store the optimal solution. It is taken as 40 in order to guide the algorithm in the search process. It prevents the algorithm from falling into a local optimal solution, but it is not too large to affect the computational efficiency. In order to test the potential solution space, a sensitivity analysis was performed on population size. Solutions were performed for population sizes of 50, 100, 150, 200 and 250 without changing other parameter settings. To address the effect of population size on the value of the objective function, an improved immuno-optimization algorithm was tested. The results show that the population size affects the initial solution level directly, and the larger the population size, the better the initial performance. As the number of iterations increases, population size is negatively correlated with affinity. The larger the population, the more stable the convergence is. However, the number of iterations required to stabilize the relationship increases. After many iterations, the population size does not have much effect on the affinity value. This is because both the maximum and minimum population size curves eventually converge to a closer affinity value. Therefore, it is reasonable to set the population size to 200, as shown in Figure 4, with Xlabel representing the number of iterations. Figure 4: Sensitivity validation of different population sizes. The unimproved immune optimization algorithm (IA) and the improved immune optimization algorithm (IIA) were selected for performance comparison. The parameter settings of the enhanced algorithm are shown in Table 4. The solution comparison with the Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO) using both algorithms according to the same parameters, respectively, is shown in Figure 5. To verify the effectiveness of the improved immune optimization algorithm, its adaptation values were compared with those of the other three basic intelligent optimization algorithms. From Figure 6, we can see that the adaptation value of IIA is higher than that of IA, GA, and PSO algorithms in the early iteration. As the number of iterations increases, the local search ability and global search ability of the IIA algorithm are further enhanced. The adaptation value of IIA algorithm is better than that of the PSO and IA algorithms. PSO and IA are more stable throughout the iterations, and the final search result is slightly better than GA's. Still, PSO tends to fall into the local optimum at the beginning of the iteration.