## 5 Experimental Outcomes
### Multi-Class Classification Experiment  
Experimentation with multi-class classification is carried out under identical test conditions as the binary classification experiment. However, a total of six classes are present in the dataset. Objective function outcomes are shown in Table 5. Indicator function outcomes are demonstrated in Table 6. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & \begin{tabular}{l} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{l} **Min Child** \\ **Weight** \\ \end{tabular} & \begin{tabular}{l} **Subsample** \\ \end{tabular} & \begin{tabular}{l} **Classple** \\ \end{tabular} & \
\begin{tabular}{l} **Max Depth** \\ \end{tabular} &
\begin{tabular}{l} **Gamma** \\ **Gamma** \\ \end{tabular} \\ \hline CNN-XG-GISA & 0.546941 & 1 & 1.000000 & 0.179374 & 10 & 0.180448 \\ CNN-XG-RSA & 0.687255 & 1 & 1.000000 & 0.010368 & 10 & 0.363584 \\ CNN-XG-GA & 0.520576 & 2 & 1.000000 & 0.23851 & 10 & 0.71737 \\ CNN-XG-PSO & 0.568677 & 5 & 1.000000 & 0.224852 & 10 & 0.80000 \\ CNN-XG-FA & 0.645137 & 1 & 1.000000 & 0.322124 & 10 & 0.800000 \\ CNN-XG-SSA & 0.621581 & 1 & 1.000000 & 0.015040 & 10 & 0.00000 \\ CNN-XG-MOOA & 0.624513 & 10 & 1.00000 & 0.01000 & 10 & 0.800000 \\ CNN-XG-COLSHADE & 0.558799 & 4 & 1.000000 & 0.226316 & 10 & 0.166346 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Control parameter selections made by each metaheuristic for respective best-performing binary classification models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
By observing the outcome, a clear superiority of the introduced algorithm can be observed, with the GIRSA attaining the best outcomes in both indicator and objective function metrics. It is also important to note the admirable stability of the WOA despite this technique not demonstrating the optimal outcomes. Algorithm stability comparisons can also be observed in Figure 4, where all tested metaheuristics are graphically compared in terms of objective and indicator functions. A significant stability improvement in terms of stability as opposed to the original RSA can be observed for the introduced GIRSA. Further improvements in algorithm convergence can be observed in Figure 5. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline CNN-XG-GIRSA & **0.755896** & **0.754757** & 0.7558369** & 0.755456 & 0.000400 & \(1.60\times 10^{-5}\) \\ CNN-XG-BSA & 0.755252 & 0.751125 & 0.753567 & 0.75486 & 0.001533 & \(2.35\times 10^{-6}\) \\ CNN-XG-GA & 0.754908 & 0.753264 & 0.754114 & 0.754263 & 0.000577 & \(3.33\times 10^{-7}\) \\ CNN-XG-PSO & 0.755196 & 0.753870 & 0.754698 & 0.754655 & 0.000475 & \(2.26\times 10^{-7}\) \\ CNN-XG-FA & 0.755564 & 0.754996 & 0.755164 & 0.754996 & 0.000226 & \(5.10\times 10^{-8}\) \\ CNN-XG-SSA & 0.754713 & 0.754328 & 0.754451 & 0.754439 & 0.000154 & \(2.38\times 10^{-8}\) \\ CNN-XG-WOA & 0.755336 & 0.754996 & 0.755095 & 0.754996 & **0.000135** & \(\mathbf{1.81\times 10^{-8}}\) \\ CNN-XG-COISHADE & 0.755119 & 0.754416 & 0.754548 & 0.754510 & 0.000323 & \(1.04\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Indicator function outcome for multi-class classification. Figure 4: Objective and indicator function distribution plots for multi-class classification. Figure 5: Objective and indicator function convergence plots for multi-class classification. As can be observed in the convergence graphs, the introduced algorithm once again demonstrates an improvement in the exploration of the search space, locating a better solution and avoiding local solutions in favor of a global solution demonstrating better outcomes. Detailed comparisons of the best-performing models generated by each metaheuristic are shown in Table 7.  
Various models show differing quality of performance facing different classification challenges. This is to be expected as per the NFL theorem. Notable outcomes are shown by the GIRSA algorithm, which has demonstrated a clear dominance in terms of optimal outcomes.