## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Experiment 1: Marine Vessel Classification  
The first set of experiments included the standard metrics applied for comparison, which were the best, worst, mean, standard deviation, and variance. Table 1 shows the overall performance for the classification error minimization, while Table 2 provides the results for the [PERSON]'s kappa coefficient for the same problem. For both cases, the proposed solution dominated all categories, resulting in an undisputed performance. Table 3 provides the results for the precision, recall, \(F_{1}\) score, and support metrics for the proposed solution. Table 4 provides the obtained hyperparameter values of the XGBoost models for each metaheuristic optimizer. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline XG-BPSO & **0.002789** & **0.003470** & **0.003100** & **0.003130** & **0.000202** & **4.09 \(\times\) 10\({}^{-8}\)** \\ XG-PSO & 0.003096 & 0.005375 & 0.004010 & 0.003929 & 0.000739 & \(5.47\times 10^{-7}\) \\ XG-GA & 0.004354 & 0.015614 & 0.007037 & 0.005511 & 0.003554 & \(1.26\times 10^{-5}\) \\ XG-ABC & 0.004898 & 0.007586 & 0.005736 & 0.005477 & 0.000817 & \(6.67\times 10^{-7}\) \\ XG-BA & 0.003368 & 0.005171 & 0.004116 & 0.004184 & 0.000559 & \(3.13\times 10^{-7}\) \\ XG-WOA & 0.003198 & 0.004762 & 0.004065 & 0.004133 & 0.000463 & \(2.14\times 10^{-7}\) \\ XG-THO & 0.003674 & 0.007076 & 0.004915 & 0.004286 & 0.001205 & \(1.45\times 10^{-6}\) \\ XG-ChOA & 0.003062 & 0.009899 & 0.004720 & 0.004133 & 0.002032 & \(4.13\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Overall objective function (classification error) results—classification error minimization experiment. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **\(\mathbf{F_{1}}\)-Score** & **Support** \\ \hline Cargo & 0.998406 & 0.999527 & 0.998966 & 16921 \\ Dredging & 0.987097 & 0.995662 & 0.991361 & 461 \\ Fishing & 1.000000 & 0.994460 & 0.99722 & 1083 \\ HSC & 0.985207 & 0.994003 & 0.989599 & 335 \\ Law enforcement & 1.000000 & 1.000000 & 1.000000 & 136 \\ Military & 0.987085 & 0.988909 & 0.987996 & 541 \\ Passenger & 0.998011 & 0.995370 & 0.996689 & 1512 \\ Pilot & 1.000000 & 0.996139 & 0.998066 & 259 \\ Pleasure & 1.000000 & 1.000000 & 1.000000 & 7 \\ Port tender & 1.000000 & 1.000000 & 1.000000 & 5 \\ Reserved & 0.937500 & 0.849057 & 0.891089 & 53 \\ SAR & 1.00000 & 0.933333 & 0.965517 & 45 \\ Sailing & 0.900000 & 0.642857 & 0.750000 & 14 \\ Tanker & 0.998580 & 0.997730 & 0.998155 & 7050 \\ Towing & 0.880000 & 0.904110 & 0.891892 & 73 \\ Towing long/wide & 0.875000 & 0.980000 & 0.924528 & 50 \\ Tug & 0.995272 & 0.988263 & 0.991755 & 852 \\ \hline accuracy & 0.997211 & 0.997211 & 0.997211 & 0.9972106 \\ macro avg & 0.973068 & 0.956438 & 0.963108 & 29397 \\ weighted avg & 0.997223 & 0.997212 & 0.997194 & 29397 \\ \hline \end{tabular}
\end{table} 
Table 3: Detailed per-class metrics for the best performing XG-BPSO metaheuristics—classification error minimization experiment. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline XG-BPSO & **0.095391** & **0.994267** & **0.994878** & **0.994829** & **0.000335** & **1.12 \(\times\) 10\({}^{-7}\)** \\ XG-PSO & 0.994886 & 0.991114 & 0.993374 & 0.993507 & 0.001223 & \(1.50\times 10^{-6}\) \\ XG-GA & 0.992803 & 0.974130 & 0.988359 & 0.990888 & 0.005892 & \(3.47\times 10^{-5}\) \\ XG-ABC & 0.991899 & 0.987456 & 0.990516 & 0.990946 & 0.001351 & \(1.83\times 10^{-6}\) \\ XG-BA & 0.994435 & 0.991452 & 0.993197 & 0.993085 & 0.000925 & \(8.56\times 10^{-7}\) \\ XG-WOA & 0.994717 & 0.992126 & 0.993282 & 0.993169 & 0.000766 & \(5.87\times 10^{-7}\) \\ XG-THO & 0.993928 & 0.988296 & 0.991873 & 0.992917 & 0.001995 & \(3.98\times 10^{-6}\) \\ XG-ChOA & 0.994942 & 0.983617 & 0.992197 & 0.993169 & 0.003366 & \(1.13\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}