## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Experiment 1: Marine Vessel Classification

Table 2: Overall indicator function ([PERSON]’s kappa) results—classification error minimization experiment. Similarly to Tables 1 and 2, Table 5 displaying [PERSON]'s kappa maximization and Table 6 for the classification error on [PERSON]'s kappa maximization indicate the superior  
Figure 4: Vessel classification confusion matrix of the proposed XG-BPSO approach. Figure 3: Vessel classification (error minimization plots) result visualization. (**a**) Objective box plot; (**b**) Objective convergence; (**c**) Precision recall curve; (**d**) Receiver operating characteristic curve; (**e**) Data diversity. and dominant performance of the proposed XG-BPSO. Table 7 provides the results of the same metrics as Table 3 for the Cohen's kappa experiments. The results of the optimal hyperparameters using metaheuristic optimizers for [PERSON]'s kappa maximization are given in Table 8. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline XG-BPSO & **0.002687** & **0.003742** & **0.003100** & **0.003045** & **0.000321** & **1.03 \(\times\) 10\({}^{-7}\)** \\ XG-PSO & 0.003402 & 0.005817 & 0.004222 & 0.003895 & 0.000803 & \(6.46\times 10^{-7}\) \\ XG-GA & 0.003572 & 0.010647 & 0.006047 & 0.005817 & 0.002024 & \(4.10\times 10^{-6}\) \\ XG-ABC & 0.004490 & 0.010171 & 0.006353 & 0.005732 & 0.001763 & \(3.11\times 10^{-6}\) \\ XG-BA & 0.003232 & 0.005749 & 0.004146 & 0.003997 & 0.000721 & \(5.21\times 10^{-7}\) \\ XG-WOA & 0.003232 & 0.006225 & 0.004218 & 0.004014 & 0.000871 & \(7.59\times 10^{-7}\) \\ XG-HHO & 0.003198 & 0.005035 & 0.003865 & 0.003691 & 0.000514 & \(2.64\times 10^{-7}\) \\ XG-ChOA & 0.003402 & 0.007008 & 0.004426 & 0.004099 & 0.001086 & \(1.18\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall indicator function (Classification Error) results—Cohen’s kappa maximization experiment. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class** & **Precision** & **Recall** & **\(\mathbf{F_{1}}\)-Score** & **Support** \\ \hline Cargo & 0.998818 & 0.999114 & 0.998966 & 16921 \\ Dredging & 0.984979 & 0.995662 & 0.990291 & 461 \\ Fishing & 1.000000 & 0.995383 & 0.997686 & 1083 \\ HSC & 0.979412 & 0.994030 & 0.986667 & 335 \\ Law enforcement & 1.000000 & 0.985294 & 0.992593 & 136 \\ Military & 0.992647 & 0.998152 & 0.995392 & 541 \\ Passenger & 0.999336 & 0.996032 & 0.997681 & 1512 \\ Pilot & 0.992337 & 1.000000 & 0.996154 & 259 \\ Pleasure & 1.000000 & 1.000000 & 1.000000 & 7 \\ Port tender & 1.000000 & 1.000000 & 1.000000 & 5 \\ Reserved & 0.921569 & 0.886792 & 0.903846 & 53 \\ SAR & 1.000000 & 0.888889 & 0.941176 & 45 \\ Sailing & 1.000000 & 0.714286 & 0.833333 & 14 \\ Tanker & 0.997732 & 0.998298 & 0.998015 & 7050 \\ Towing & 0.942857 & 0.904110 & 0.923077 & 73 \\ Towing long/wide & 0.842105 & 0.960000 & 0.897196 & 50 \\ Tug & 0.994097 & 0.988263 & 0.991171 & 852 \\ \hline accuracy & 0.997313 & 0.997313 & 0.997313 & 0.997313 \\ macro avg & 0.979170 & 0.959077 & 0.967250 & 29397 \\ weighted avg & 0.997346 & 0.997313 & 0.997303 & 29397 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed per-class metrics for the best performing XG-BPSO metaheuristics—[PERSON]’s kappa maximization experiment.