## 4 Experimental Simulation and Analysis
### Simulation Result Analysis
#### 4.2.2 Simulation Results and Analysis  
In order to verify the optimizing ability of the IBES algorithm proposed in this paper, the BES algorithm and the IBES algorithm are compared on the basis of using the same experimental parameters. To avoid lengthy articles, six representative functions were selected for experiments, namely: unimodal benchmark function (\(F_{1}\),\(F_{2}\)), multimodal benchmark function (\(F_{3}\),\(F_{4}\)), and fixed-dimensional multimodal benchmark function (\(F_{5}\),\(F_{6}\)). See Table 6 for details. Using the convergence curves of different objective functions to compare the performance of the two algorithms, the convergence curves are shown in Figure 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Benchmark Function** & \begin{tabular}{c} **Dimen-** \\ **sion** \\ \end{tabular} & \
\begin{tabular}{c} **Search** \\ **Scope** \\ \end{tabular} &
\begin{tabular}{c} **Theoretical** \\ **Minimum** \\ \end{tabular} \\ \hline \(F_{1}(x)=\sum\limits_{i=1}^{n}(x_{i}-90)^{2}\) & 30 & \([-100,100]\) & 0 \\ \(F_{2}(x)=\sum\limits_{i=1}^{n}(|x_{i}+0.5|)^{2}\) & 30 & \([-100,100]\) & 0 \\ \(F_{3}(x)=\sum\limits_{i=1}^{n}-x_{i}\sin(\sqrt{|x_{i}|})\) & 30 & \([-500,500]\) & \(-12\),\(569.46\) \\ \(F_{4}(x)=\frac{\pi}{n}\{10\sin(\pi y_{1})+\sum\limits_{i=1}^{n-1}(y_{i}-1)^{ 2}[1+10\sin^{2}(\pi y_{i+1})]+(y_{n}-1)^{2}\}+\sum\limits_{i=1}^{n}u(x_{i},10,100,4)\) & 30 & \([-50,50]\) & 0 \\ \(y_{i}=1+\frac{x_{i}+1}{4}\) & & & \\ \(F_{5}(x)=\sum\limits_{i=1}^{11}[a_{i}-\frac{x_{i}(b_{i}^{2}+b_{i}x_{3})}{b_{i}^ {2}+b_{i}x_{3}+x_{4}}]^{2}\) & 4 & \([-5,5]\) & 0.00030 \\ \(F_{6}(x)=-\sum\limits_{i=1}^{4}c_{i}\exp(-\frac{6}{j=1}a_{ij}(x_{i}-p_{ij})^{ 2})\) & 6 & \([0,1]\) & \(-3.32\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Benchmark function. From Figure 2(a),b, it can be seen that when dealing with unimodal reference functions, the IBES algorithm can achieve convergence faster and has the stronger optimization ability compared to the original algorithm. Figure 2(c),d show that the IBES algorithm has better performance when dealing with multimodal reference functions as the introduced Levy flight and reverse learning strategies can help the algorithm jump out of local optima. It can be seen from Figure 2(e),f that the IBES algorithm still has a faster rate of convergence and stronger search ability in the face of fixed dimensional multimodal reference function. Therefore, the bald eagle optimization algorithm improved by chaos initialization, Levy flight, and reverse learning strategy in this paper has a faster rate of convergence and stronger optimization ability when facing different types of benchmark functions. When the number of available time slots in the system is 700, analyze the number of time slots allocated by the five algorithms for each user. Because the number of users is large, it is difficult to visually present the allocation results of different algorithms, so the top six users are selected from the four groups of users in Table 3 for analysis, and the results are shown in Figure 4.  
Figure 3: Benchmark function convergence curve. From Figure 4, it can be seen that when the number of available time slots is 700, the five algorithms allocate a different number of time slots for each user. The IHQB algorithm allocates more time slots for users 4, 37, and 39, while allocating fewer time slots for users 108 and 111. This is because the algorithm only considers the user's business priority and ignores the impact of channel state information in the process of allocating resources to users, thereby allocating too much bandwidth to some users with poor channel state. The BABEE algorithm allocates a relatively average number of time slots to users as it only considers the user's minimum rate constraint during the allocation process and ignores the bandwidth requirements of users with different priority levels. The BO-CL-DBA algorithm allocates fewer time slots for users 4, 36, and 75.