## 4 Experimental Verification and Results Analysis
### Comparative Analysis of Simulation Results
In other task scenarios, the performance of the MSIDBO algorithm was significantly better than that of other algorithms, which indicates that the MSIDBO algorithm has significant advantages when solving cross-domain collaborative task allocation problems. For small-, medium-, and large-scale task scenarios, the optimization performance of the MSIDBO algorithm was 28.9-55.8%, 22.7-77%, and 14.6-62.4% higher than that of classical algorithms such as PSO, and 3.5-31.4%, 8.5-76.1%, and 2.1-62.1% higher than that of the original DBO algorithm and other existing improved forms, among which the index of the MODBO algorithm was not counted in the control allocation. 2. Running time: It can be seen from Figures 7-10 that, with an increase in the problem scale, the time for each algorithm to fall into the local optimal solution increased, and the number of iterations required to jump out of the local optimal solution increased continuously. The average running time of MSIDBO was close to that of DBO, PSO, SSA, and GWO and much lower than that of MODBO, MSADBO, IDBO, and other improved dung beetle algorithms, which indicates that MSIDBO has high computational efficiency. 3. Convergence: It can be seen from Figures 7-10 that the initial function value of the MSIDBO algorithm was large; it converged rapidly in the early iterations and the convergence index dropped sharply. This indicates that the solution space of the initial allocation scheme generated by the algorithm was uniform and had strong diversity, and the introduced fusion Fuch chaotic mapping and reverse learning strategy had obvious effects. In the middle iteration, the time to fall into the local optimal solution was less than that of other algorithms, indicating that the algorithm can quickly jump out of the local optimal solution and balance the contradiction between a global search and a local search. In summary, compared with other algorithms, the MSIDBO algorithm generates an initial population with a uniform distribution and strong diversity in the early iteration of the algorithm; it can quickly jump out of the local optimal solution in the middle of the algorithm; and it can obtain the global optimal solution with the lowest fitness at the end of the algorithm. The MSIDBO algorithm can adapt to task allocation problems of different scales, and can better balance global optimization and efficient calculations. The LSD significance test and a comparative analysis showed that the MSIDBO algorithm has significant advantages in solving cross-domain collaborative task allocation problems, and  
Figure 12: LSD significance test for control allocation and statistical analysis of results. the solution obtained has a good convergence and distribution. The real-time performance of the algorithm is also strong, and the solution speed is significantly better than that of other improved algorithms. Finally, an effective and feasible task allocation scheme was obtained. Although the MSIDBO algorithm demonstrated superior performance, it still has the following problems:  
1. Computational Resource Utilization: The MSIDBO algorithm needs to replace the beetle population that exceeds the constraint boundary when updating the position in each iteration, and the fitness function is called frequently, which occupies a large amount of system computing resources and increases the iteration time cost. In practical tasks, the replacement set of dung beetle individuals should be generated in advance, and the replacement should be called in order. 2. Parameter Tuning Challenges: In the adaptive rolling dung beetle population reduction strategy, we have innovatively designed the proportion of rolling dung beetles to decrease as the number of iterations increases. Consequently, the proportion of the other three types of dung beetles also varies.