# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment  
### Simulation Parameterization  
To evaluate the performance of the QSA algorithm, a series of simulation experiments are conducted in this paper. The coordinates of users and base stations are randomly generated in a 10 km \(\times\) 10 km area and follow a uniform distribution. Meanwhile, to ensure the repeatability of the experiments, a fixed random seed is used. The assumption of uniform distribution is representative of many real-world scenarios, and it can effectively model the uniform coverage of users and base stations in a given area. Generating user \begin{table}
\begin{tabular}{l c} \hline
1: & for \(i=1:N\) \\
2: & for \(d=1:D\) \\
3: & if \(rand>Pr_{i}\) \\
4: & Randomly select two individuals \(X_{r1}\) and \(X_{r2}\) \\
5: & Obtain \(X_{i}^{new}\) based on Equation (13) \\
6: & end if \\
7: & end for \\
8: & Accept \(X_{i}^{new}\) if it obtains a better fitness function value \\
9: & end for \\ \hline \end{tabular}
\end{table} 
Table 4: Pseudo code of update procedure in business 3. \begin{table}
\begin{tabular}{l c} \hline
1: & for \(i=1:N\) \\
2: & if \(i\leq q_{21}\) \\
3: & \(A=A_{21}\) \\
4: & else if \(i>q_{21}\) and \(i\leq q_{21}+q_{22}\) \\
5: & \(A=A_{22}\) \\
6: & else \\
7: & \(A=A_{23}\) \\
8: & end if \\
9: & if \(rand<Pr_{i}\) \\
10: & Randomly select two individuals \(X_{r1}\) and \(X_{r2}\) \\
11: & if \(rand<C_{p}\) \\
12: & Obtain \(X_{i}^{new}\) based on Equation (10) \\
13: & else \\
14: & Obtain \(X_{i}^{new}\) based on Equation (11) \\
15: & end if \\
16: & Accept \(X_{i}^{new}\) if it obtains a better fitness function value \\
17: & end if \\
18: & end for \\ \hline \end{tabular}
\end{table} 
Table 3: Pseudo code of update procedure in business 2.  
and base station information in this way helps to verify the robustness and applicability of QSA in dealing with complex computational problems. The performance evaluation metric for the experiment is the hop count of the user request. A lower hop count indicates that a user request is forwarded through the network less often, thus enabling lower transmission and processing delays in the absence of channel noise interference. Therefore, the placement of base stations in this experiment is random, without considering physical proximity, but ensuring that all users are covered by at least one base station. The experimental software environment consists of PyCharm 2022.3.3 (community edition) and Python 3.9.13. The hardware environment consists of an Intel i7-12700H processor (Santa Clara, CA, USA), 16 GB RAM, and an NVIDIA RTX 3050 GPU (Santa Clara, CA, USA). In the simulation experiments, the number of users, the number of base stations, and the number of edge servers are preset, and the locations of users and base stations are known. Other experimental parameters are shown in Table 5.  
The ranges of the input data are mainly set based on reasonable assumptions of actual application scenarios, along with the typical scale of edge computing deployments, to ensure the feasibility and representativeness of the experiments. The following is the basis for determining the range of different parameters:  
1. Number of users (1000-10,000)  
Setting a range of 1000-10,000 can cover small to medium-scale MEC deployments, both to demonstrate the fast convergence of the algorithm on small-scale problems and to test its scalability in large-scale scenarios. 2. Number of base stations (80-200)  
The number of base stations depends on the subscriber density and coverage area, and usually, operators deploy more base stations in high-density areas (e.g., cities) to provide better quality of service. The range of 80-200 is in line with the typical cellular network deployment model and ensures that sufficient base station resources can still be provided when the subscriber number changes. 3. BTS network density (0.01-0.1)  
In reality, base station density is typically higher in urban environments and lower in suburban areas, so setting 0.01-0.1 simulates different types of deployment environments. 4.