## 5 Simulation Experiment
### Experimental Result
The number of tasks is 30, 45, 60 and 75, respectively, and the number of sensor nodes is 16, 24, 32 and 40, respectively. The network income gap of each algorithm can be visually seen through the bar chart. In Figure 6a, although the network benefits of the CEAGA are higher than those of the other three algorithms, the advantages of the CEAGA are not obvious. Because the number of tasks is small, the other three algorithms can obtain good network benefits in this case. In Figure 6b, the advantages of the CEAGA are further improved, and the advantages of the other three algorithms are obviously inferior to Figure 6a. Similar analysis results can be obtained from Figure 6c,d. It can be seen that when the number of sensors and tasks in IUWSNs increases, the network revenue of the HGA, MBPSO and ISA will increase, but the algorithm performance will gradually decline. The CEAGA has the support of chaotic operators, which can ensure the randomness of population generation and help to find the optimal solution in the multitask number. In addition, elite operators and adaptive operators can ensure that good genes are retained, and bad genes are eliminated. Therefore, in different cases, the total network revenue of the CEAGA is greater than that of the HGA, MBPSO and ISA. As the number of tasks and sensors increases, the network revenue of the CEAGA will also increase, and the performance of the algorithm is good and stable. Figure 5: The above four figures show the comparison of network revenue optimization trends of the CEAGA, HGA, MBPSO and ISA algorithms, respectively: the algebra in (**a**–**d**) is 1000. (**a**) shows the operation results of 30 tasks and 16 sensor nodes; (**b**) shows the operation results of 45 tasks and 24 sensor nodes; (**c**) shows the operation results of 60 tasks and 32 sensor nodes; (**d**) shows the operation results of 75 tasks and 40 sensor nodes. Table 7 shows the network revenue growth rate of the CEAGA compared with the other three algorithms. For a scenario where there are 30 tasks and 16 sensors, the CEAGA demonstrates a network revenue that is 8.1% higher than the HGA, 7.4% higher than the MBPSO, and 9.0% higher than the ISA. For a given configuration where the number of tasks is 45 and the number of sensors is 24, the network revenue of the CEGA is 11.5% higher than the GA, 10.1% higher than the MBPSO, and 13.1% higher than the SA. Similar conclusions can be obtained in the other two parameter environments. It is evident that as the number of tasks and sensors increases, the network revenue growth rate of the CEAGA gradually increases and the advantages of the CEAGA gradually expand. Therefore, the network revenue effect of the CEAGA is better for the number of tasks and the number of multi-sensor nodes. Table 8 shows the running time of the CEAGA, HGA, MBPSO and ISA under different task numbers and flow sensor nodes. The table demonstrates a clear trend of increased running time for each algorithm as the number of tasks and sensor nodes grows. The running time of the CEAGA is the shortest, followed by the MBPSO, then HGA and the running time of the ISA is the largest. With the support of chaos operator, elite operator and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Tasks** & **Number of Sensor Nodes** & **CEAGA** & **HGA** & **MBPSO** & **ISA** \\ \hline
30 & 16 & 8.1\% & 7.4\% & 9.0\% \\
45 & 24 & 11.5\% & 10.1\% & 13.1\% \\
60 & 32 & 14.9\% & 13.0\% & 15.8\% \\
75 & 40 & 16.5\% & 14.7\% & 17.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage growth rate of the CEAGA compared with the other three algorithms. Figure 6: **(a–d)** shows the task allocation network benefits of the three algorithms. The maximum algebra of the 3 algorithms is 1000, including 16, 24, 32, 40 sensor nodes and 30, 45, 60, 75 tasks, respectively. In order to more intuitively see the difference between the results of the three algorithms, the bar graph is used to draw the results. adaptive operator, the CEAGA is optimized.