# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Analysis of the Selected Optimization Methods
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter Name** & **Value** & **Description** \\ \hline \multicolumn{3}{c}{**I-GWO**} \\ \hline Max iterations & **100** & The upper limit on iterations permitted without a stall \\ Max stall iterations & **15** & The highest allowable number of successive iterations without finding an improved solution \\ Search agents & 100 & Number of wolves, i.e., potential solutions \\ Function tolerance & **1** & The min. cost change required to avoid being classified as a stall \\ \hline \multicolumn{3}{c}{**PSO**} \\ \hline Max iterations & **150** & The upper limit on iterations permitted without a stall \\ Max stall iterations & **25** & The highest allowable number of successive iterations without finding an improved solution \\ Swarm size & **400** & The particle count in the swarm \\ Self adj. weight & 1.49 & The weight of each particle’s best position in velocity adjustment \\ Social adj. weight & 1.49 & The weight of each neighborhood’s best position in velocity adjustment \\ Min neighbors fraction & **0.5** & The smallest size of the adaptive neighborhood \\ Function tolerance & **1** & The min. cost change required to avoid being classified as a stall \\ \hline \multicolumn{3}{c}{**GA**} \\ \hline Max generations & 3700 & The upper limit on generations permitted without a stall \\ Stall gen limit & **20** & The highest allowable number of successive iterations without finding an improved solution \\ Population size & **1500** & Number of population members \\ Crossover ratio & 1.2 & How far the child is from the better parent in the crossover function \\ Crossover fraction & 0.8 & The number of non-elite children produced in the next generation \\ Function tolerance & **1** & The min. cost change required to avoid being classified as a stall \\ \hline \multicolumn{3}{c}{**ACO\({}_{\mathbb{R}}\)**} \\ \hline Max iterations & **300** & The upper limit on iterations permitted without a stall \\ Max stall iterations & **15** & The highest allowable number of successive iterations without finding an improved solution \\ Sample size & **300** & The count of solutions evaluated during each iteration \\ Archive size & **50** & Number of the best solutions (ants) from the previous iteration \\ Deviation distance ratio & 0.85 & How far the new solutions can deviate from their source \\ Intensification factor & 0.001 & The likelihood of selecting the best solution to generate new solutions during an update \\ Function tolerance & **1** & The min. cost change required to avoid being classified as a stall \\ \hline \multicolumn{3}{c}{Non-default algorithm settings are highlighted in bold.} \\ \end{tabular}
\end{table} 
Table 2: Optimization algorithms—selected parameters. In this example, no collisions take place, so all paths are valid. The generated measurement paths are situated near the area of maximum pollution. However, as highlighted in Table 3, the algorithms' scores vary significantly. The path with the best COST was produced by ACO\({}_{\mathbb{R}}\). This is confirmed by the LEN, NPTS, and EEE metrics, which classify it as the shortest and most energy-efficient path. Notably, ACO\({}_{\mathbb{R}}\) also achieved the lowest CT, even though I-GWO and PSO had considerably fewer CFEs. ACO\({}_{\mathbb{R}}\) showed quicker convergence than the other algorithms, producing a noticeably short path. As a result, the subsequent cost function evaluations resulted in significant CT savings, even with an increased number of CFEs. It is important to note that this experiment does not include a simulation phase. As a result, EEE is calculated under the assumption of zero wind speed, similar to the energy expenditure criterion (\(v_{g}=v_{a}\)). To obtain more accurate results, EEE should be computed using simulation data. Table 4 presents fundamental statistical summaries of the metrics calculated for the first scenario across all algorithm runs. Figure 4 offers a concise visual overview of the path variability observed in each algorithm across consecutive runs.