## 4 Experimental Verification and Results Analysis
### Comparative Analysis of Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Mission** \\ **Scenario** \\ \end{tabular} } & \multirow{3}{*}{**Algorithm**} & \multicolumn{3}{c}{**Target Allocation**} & \multicolumn{3}{c}{**Task Allocation**} \\ \cline{3-8}  & & \begin{tabular}{c} **Average Run** \\ **Time (in Miliseconds)** \\ \end{tabular} & \begin{tabular}{c} **Global** \\ **Optimum** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Performance** \\ **Comparison** \\ \end{tabular} & \begin{tabular}{c} **Average Run** \\ **Time (in Miliseconds)** \\ \end{tabular} & \begin{tabular}{c} **Global** \\ **Optimum** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Performance** \\ **Comparison** \\ \end{tabular} \\ \hline \hline \multirow{7}{*}{\begin{tabular}{c} Mission 1 \\ \end{tabular} } & MSDBO & 44.03 & **0.2123** & 0.0\% & 70.94 & **0.0973** & 0.0\% \\ \cline{2-8}  & MODBO & 74.35 & 0.2200 & 3.5\% & 120.66 & 0.0976 & 0.4\% \\ \cline{2-8}  & MSADBO & 76.19 & 0.2479 & 14.4\% & 161.46 & 0.1137 & 14.4\% \\ \cline{2-8}  & IDBO & 111.87 & 0.2893 & 26.6\% & 181.73 & 0.1043 & 6.7\% \\ \cline{2-8}  & DBO & **37.86**1  & 0.3093 & 31.4\% & 61.79 & 0.1072 & 9.3\% \\ \cline{2-8}  & PSO & 37.88 & 0.2987 & 28.9\% & **59.02** & 0.2202 & 55.8\% \\ \cline{2-8}  & SSA & 41.34 & 0.3234 & 34.4\% & 67.04 & 0.1788 & 45.6\% \\ \cline{2-8}  & GWO & 38.73 & 0.3152 & 32.7\% & 61.39 & 0.1385 & 29.8\% \\ \hline \multirow{7}{*}{\
\begin{tabular}{c} Mission 2 \\ \end{tabular} } & MSDBO & 100.45 & **0.0504** & 0.0\% & 373.38 & 0.0526 & 0.0\% \\ \cline{2-8}  & MODBO & 181.96 & 0.0698 & 27.8\% & 371.84 & **0.0521** & \(-1.0\%\) \\ \cline{2-8}  & MSADBO & 187.06 & 0.1439 & 65.0\% & 371.61 & 0.0726 & 27.5\% \\ \cline{2-8}  & IDBO & 272.44 & 0.2070 & 75.7\% & 453.01 & 0.0575 & 8.5\% \\ \cline{2-8}  & DBO & 94.63 & 0.2112 & 76.1\% & 190.40 & 0.0771 & 31.8\% \\ \cline{2-8}  & PSO & **92.80** & 0.2171 & 76.8\% & **186.54** & 0.0681 & 22.7\% \\ \cline{2-8}  & SSA & 101.33 & 0.2191 & 77.0\% & 206.49 & 0.0979 & 46.3\% \\ \cline{2-8}  & GWO & 94.58 & 0.2133 & 76.4\% & 187.58 & 0.0939 & 43.9\% \\ \hline \multirow{7}{*}{
\begin{tabular}{c} Mission 3 \\ \end{tabular} } & MSDBO & 240.60 & **0.6051** & 0.0\% & 622.11 & **0.0336** & 0.0\% \\ \cline{2-8}  & MODBO & 451.76 & 0.6182 & 2.1\% & 1017.60 & 0.0338 & 0.5\% \\ \cline{2-8}  & MSADBO & 476.14 & 0.6513 & 7.1\% & 1021.23 & 0.0686 & 51.1\% \\ \cline{2-8}  & IDBO & 653.71 & 0.6984 & 13.4\% & 1503.46 & 0.0873 & 61.5\% \\ \cline{2-8}  & DBO & **221.76** & 0.7112 & 14.9\% & 519.08 & 0.0887 & 62.1\% \\ \cline{2-8}  & PSO & 224.37 & 0.7113 & 14.9\% & **509.11** & 0.0851 & 60.5\% \\ \cline{2-8}  & SSA & 246.66 & 0.7173 & 15.6\% & 561.93 & 0.0892 & 62.4\% \\ \cline{2-8}  & GWO & 228.83 & 0.7084 & 14.6\% & 512.85 & 0.0888 & 62.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation performance of each algorithm. Figure 11: LSD significance test of target allocation and statistical analysis of results. After deeply analyzing and comparing a variety of task allocation algorithms, we pay special attention to the performance of the MSIDBO algorithm in cross-domain collaborative task allocation problems. By comparing the MSIDBO algorithm with other classical and improved algorithms in terms of optimization performance, running time and convergence, the advantages of the MSIDBO algorithm can be comprehensively assessed. By comparing and analyzing the data in Figures 7-12 and Table 5, the following conclusions can be drawn:  
1. Optimization performance: As can be seen from the data in Figures 7 and 8 and Table 5, the solving performance of the MSIDBO algorithm for the control allocation model was close to that of MODBO, but the solving time was greatly shortened. Figure 12 shows that there were no significant differences between the two algorithms.