## 4 Simulation Experiment of IABCBAS Algorithm Path Planning Application  
### Establishment of Environmental Models  
The IABCBAS algorithm is capable of rapidly generating a large number of waypoints, conducting optimal path searches, and enabling mobile platforms to avoid obstacles in the environment through path planning, thereby enhancing the operational efficiency and environmental adaptability of the platforms. Particularly in path planning for field environments such as military operations, this algorithm can give full play to the advantages of search algorithms in solving high-dimensional space problems; it efficiently explores the optimal solutions for path planning in three-dimensional space and optimizes the kinematic constraints and performance constraints of mobile platforms. Therefore, the IABCBAS algorithm holds high application value in addressing path planning problems across mul \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{\(\mathbf{f_{1}}\) **Function**} & \multicolumn{2}{c}{\(\mathbf{f_{2}}\) **Function**} & \multicolumn{2}{c}{\(\mathbf{f_{3}}\) **Function**} \\ \cline{2-7}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline ABC & \(3.243\times 10^{-9}\) & \(5.003\times 10^{-8}\) & \(3.132\times 10^{-1}\) & \(4.233\times 10^{-1}\) & \(4.431\times 10^{-4}\) & \(3.165\times 10^{-3}\) \\ ACO1 & \(1.605\times 10^{-20}\) & \(2.327\times 10^{-19}\) & \(4.292\times 10^{-2}\) & \(3.482\times 10^{-2}\) & \(2.076\times 10^{-8}\) & \(1.297\times 10^{-7}\) \\ ACO2 & \(1.763\times 10^{-17}\) & \(2.301\times 10^{-16}\) & \(3.914\times 10^{-3}\) & \(2.631\times 10^{-2}\) & \(2.435\times 10^{-8}\) & \(1.371\times 10^{-7}\) \\ ACO3 & \(3.363\times 10^{-27}\) & \(2.092\times 10^{-26}\) & \(2.472\times 10^{-11}\) & \(4.972\times 10^{-10}\) & \(6.316\times 10^{-31}\) & \(2.124\times 10^{-30}\) \\ BAS1 & \(1.026\times 10^{-22}\) & \(1.924\times 10^{-21}\) & \(5.594\times 10^{-7}\) & \(2.741\times 10^{-6}\) & \(4.291\times 10^{-19}\) & \(1.137\times 10^{-18}\) \\ BAS2 & \(5.084\times 10^{-21}\) & \(5.221\times 10^{-20}\) & \(6.357\times 10^{-6}\) & \(4.738\times 10^{-5}\) & \(4.578\times 10^{-18}\) & \(1.119\times 10^{-17}\) \\ BAS3 & \(5.675\times 10^{-26}\) & \(3.929\times 10^{-25}\) & \(4.771\times 10^{-10}\) & \(3.829\times 10^{-9}\) & \(5.581\times 10^{-24}\) & \(2.186\times 10^{-23}\) \\ IABCBAS & \(4.076\times 10^{-29}\) & 0 & \(3.122\times 10^{-13}\) & \(2.891\times 10^{-12}\) & 0 & 0 \\ \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{\(\mathbf{f_{4}}\) **function**} & \multicolumn{2}{c}{\(\mathbf{f_{5}}\) **function**} & \multicolumn{2}{c}{\(\mathbf{f_{6}}\) **function**} \\ \cline{2-7}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline ABC & \(2.698\) & \(1.627\) & \(-3.133\times 10^{2}\) & \(3.156\times 10^{2}\) & \(1.957\times 10^{-2}\) & \(6.452\times 10^{-4}\) \\ ACO1 & \(2.063\) & \(1.614\) & \(-4.497\times 10^{2}\) & \(3.661\times 10^{2}\) & \(1.204\times 10^{-2}\) & \(7.442\times 10^{-4}\) \\ ACO2 & \(2.274\) & \(2.014\) & \(-4.398\times 10^{2}\) & \(3.349\times 10^{2}\) & \(1.705\times 10^{-2}\) & \(7.452\times 10^{-4}\) \\ ACO3 & \(0\) & \(0\) & \(-8.367\times 10^{2}\) & \(8.427\times 10^{2}\) & 0 & 0 \\ BAS1 & \(0\) & \(0\) & \(-6.823\times 10^{2}\) & \(5.391\times 10^{2}\) & 0 & 0 \\ BAS2 & \(0\) & \(0\) & \(-6.332\times 10^{2}\) & \(5.665\times 10^{2}\) & 0 & 0 \\ BAS3 & \(0\) & \(0\) & \(-7.422\times 10^{2}\) & \(7.308\times 10^{2}\) & 0 & 0 \\ IABCBAS & \(0\) & \(0\) & \(-5.282\times 10^{3}\) & \(5.511\times 10^{3}\) & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test results of each algorithm on the benchmark function. tiple engineering application fields.