## 4 Simulation Experiment and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**UAV\({}^{*}\)**} & \multicolumn{2}{c}{**Statistical**} & \multirow{2}{*}{**CC-MPSO**} & \multirow{2}{*}{**PSO**} & \multirow{2}{*}{**MPSO**} & \multirow{2}{*}{**ECPO-ME**} & \multirow{2}{*}{**ACO**} & \multirow{2}{*}{**RI-MAC**} \\  & & **Analysis** & & & & & & \\ \hline \multirow{8}{*}{UAV1} & BEST & **0.15581** & 0.11235 & 0.12135 & 0.13153 & 0.12654 & 0.14985 \\  & WORST & **0.13516** & 0.08486 & 0.10135 & 0.11235 & 0.09846 & 0.13158 \\  & MEAN & **0.14155** & 0.10211 & 0.11232 & 0.12021 & 0.11205 & 0.14029 \\  & MEDIAN & 0.14551 & 0.11213 & 0.10756 & 0.11954 & 0.10984 & **0.14689** \\  & SD & 0.00483 & 0.01321 & 0.00865 & **0.00315** & 0.00846 & 0.00532 \\ \hline \multirow{8}{*}{UAV2} & BEST & **0.15305** & 0.13155 & 0.12895 & 0.13158 & 0.12156 & 0.15123 \\  & WORST & 0.13215 & 0.11215 & 0.10184 & 0.11218 & 0.09154 & **0.13847** \\  & MEAN & **0.14516** & 0.12021 & 0.11055 & 0.12157 & 0.11484 & 0.14084 \\  & MEDIAN & **0.14984** & 0.12566 & 0.11846 & 0.12786 & 0.11846 & 0.14235 \\  & SD & **0.00485** & 0.00858 & 0.00684 & 0.01325 & 0.00987 & 0.00865 \\ \hline \multirow{8}{*}{UAV3} & BEST & 0.14041 & 0.10513 & 0.11235 & 0.12154 & 0.11235 & **0.14335** \\  & WORST & 0.12015 & 0.08356 & 0.09021 & 0.10213 & 0.08456 & **0.12154** \\  & MEAN & **0.13031** & 0.09128 & 0.10212 & 0.11235 & 0.10215 & 0.12485 \\  & MEDIAN & 0.12832 & 0.09513 & 0.10514 & 0.10984 & 0.10465 & **0.12984** \\  & SD & **0.00516** & 0.00684 & 0.00846 & 0.01032 & 0.00846 & 0.00784 \\ \hline \multirow{8}{*}{UAV4} & BEST & **0.15118** & 0.12032 & 0.13151 & 0.14651 & 0.12123 & 0.14987 \\  & WORST & 0.12021 & 0.09125 & 0.10213 & 0.11238 & 0.09865 & **0.12346** \\ \cline{1-1}  & MEAN & **0.13456** & 0.10218 & 0.11215 & 0.12354 & 0.10125 & 0.12354 \\ \cline{1-1}  & MEDIAN & **0.13516** & 0.11098 & 0.11568 & 0.11984 & 0.11523 & 0.12865 \\ \cline{1-1}  & SD & 0.00746 & 0.00648 & 0.01028 & 0.00788 & 0.00589 & **0.00424** \\ \hline \end{tabular}
\end{table} 
Table 7: Statistical analysis of the outcome of all algorithms for scenario 6. Figure 8: Search path for scenario 2 generated by the tested algorithms. Figure 7: Search path for scenario 1 generated by the tested algorithms. 3. Scenario 3 The results for scenario 3 are tabulated in Table 4. The acquired results indicate the following conclusions: * For UAV1, the CC-MPSO algorithm achieves optimal results in the three statistical parameters \"BEST\", \"MEAN\", and \"MEDIAN\". \"RI-MAC\" achieves the best results in \"WORST\" and \"SD\". * For UAV2, the CC-MPSO algorithm achieves the best results in \"BEST\", \"WORST\", \"MEAN\", and \"SD\"; RI-MAC achieves the best results in \"MEDIAN\"; and RI-MAC achieves optimal results in \"BEST\", \"WORST\", \"MEAN\", and \"SD\". Figure 9 displays the most efficient search routes produced by all the algorithms that were tested in scenario 3. The purple trajectory represents the flight route of UAV1, whereas the white trajectory represents the flight path of UAV2. UAV1 starts at the bottom left corner of the map, which is near the high probability target area. The CC-MPSO algorithm enables UAV1 to search in the direction of the target's path. UAV2 initially moves in the opposite direction of the target during its search. As the target moves further away, UAV2 can dynamically change its search direction to the opposite side. This demonstrates the strong adaptive capability of the CC-MPSO algorithm when searching along the diagonal. (4) Scenario 4 The results for scenario 4 are tabulated in Table 5. The acquired results indicate the following conclusions: * For UAV1, the CC-MPSO algorithm achieves optimal results in \"BEST\", \"MEAN\", and \"MEDIAN\", while the RI-MAC algorithm achieves optimal results in the rest of the statistical parameters. * For UAV2, CC-MPSO achieves optimal results in \"BEST\" and \"MEDIAN\", while RI-MAC achieves optimal results in the rest of the statistical parameters.