## 4 Simulation Experiments and Data Analysis
### Analysis of Experimental Results
The solution can also be more highly applicable to variable networks, quickly adapting to network state changes through a dynamic task allocation mechanism. In scenarios such as signal fluctuations and bandwidth limitations in 5G networks, tasks can be assigned to more appropriate nodes in a timely manner, avoiding task backlogs and performance degradation caused by network instability. Figure 11: The relationship between the number of tasks and the average latency of the system. Figure 10: The relationship between the number of tasks and the average energy consumption of the system. In order to further compare the stability of the scheme, the FOX scheme, the Aurora optimization scheme, and the improved Aurora optimization scheme are tested under different constraints (fixed amount of data and number of tasks, fixed CPU cycles and number of tasks, fixed CPU cycles and amount of data). Table 2 demonstrates the stability performance of the algorithm overhead under different conditions. From the results, it can be seen that the IPLO scheme has the smallest standard deviation in the test scenarios, indicating that the algorithm has high stability compared to the FOX scheme and PLO scheme, which have larger standard deviations. The reason is that the FOX scheme is prone to ineffective searching at the edges of the solution space due to the lack of a specialized design for the boundary problem; although the PLO scheme possesses better search capability, the boundary truncation processing leads to a rapid decline in population diversity at the later stage of the iteration. Therefore, the improved boundary processing strategy significantly improves the stability of the algorithm in complex optimization scenarios. In order to analyze the sensitivity of the IPLO scheme, we systematically investigate the impact of different thresholds on the performance of the algorithm by controlling the probability of particle collision in the mutation operation. The probability of particle collision directly determines how often the algorithm introduces random perturbations during the search process, and by adjusting this threshold, we can explore the adaptive ability of the algorithm under different environmental conditions. Same as the constraints for testing the stability of the algorithm, we test the algorithm in terms of fixed data volume and number of tasks, fixed CPU cycles and number of tasks, and fixed CPU cycles and data volume, respectively. Table 3 demonstrates the sensitivity performance of the average overhead of the IPLO algorithm under different collision probabilities. The experimental results show that \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arithmetic** & \begin{tabular}{c} **Fixed Data Volume** \\ **and Number of Tasks** \\ \end{tabular} & \
\begin{tabular}{c} **Fixed CPU Cycles and** \\ **Number of Tasks** \\ \end{tabular} &
\begin{tabular}{c} **Fixed CPU Cycles** \\ **and Data Volume** \\ \end{tabular} \\ \hline FOX & 0.022325712 & 0.121529847 & 0.084908155 \\ PLO & 0.006160379 & 0.120456452 & 0.086015252 \\ IPLO & 0.001792003 & 0.115658619 & 0.071276243 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Standard deviations of algorithmic overhead under different conditions. Figure 12: The relationship between the number of tasks and the average overhead of the system. the average overhead of the algorithm does not show a significant difference when the collision probability threshold is varied in the range of 0.01 to 0.1. The reason is that IPLO is forced to keep the globally optimal particles in each generation during the iteration process, and the update of other particles is strictly guided by the optimal particles. Even if the collision probability threshold is high, newly generated randomized perturbation solutions that are inferior to the current optimal solution will be directly discarded.