## 5 Results and Discussion
### Random Forest on UCI Datasets  
Table 4 shows the performance of the four metaheuristic algorithms when optimizing the hyperparameters of random forest (RF) model based on four UCI datasets. The \"Time\" column shows the execution time, the column \"Best Pos\" shows the values of the best hyperparameters after the optimization run. The column \"Best Sol\" is the accuracy obtained for that run when using the best hyperparameters for that run. The \"Iter\" column shows the number of iterations it takes for the algorithm to arrive at that accuracy. The variation of the prediction accuracies for all the optimization algorithms is shown in Figure 7.  
Figure 6: Validation accuracy of CNN model as iteration progresses. Figure 7: Prediction accuracy for the different algorithms under the different UCI datasets. We present in Table 4 the results obtained when training the RF model on five UCI datasets using the BO, SDSGA, GA, PSO and BBO algorithms to tune the HPs. In some cases, the best solution obtained by each optimization algorithm (BO, SDSGA, GA, PSO and BBO) was the same as demonstrated by the Breast Cancer dataset, where all optimization algorithms achieved an accuracy of 98.246% despite being obtained under different HPs values (see Table 4B). This is because the multiple hyperparameter combinations produced the same validation accuracy in this problem. Because the outcome is heavily dependent on the initial population, this can have a significant impact on the efficiency of metaheuristic optimization algorithms. In the CAR dataset (Table 4A), it is noticed that both the BO and SDSGA algorithms performed the best at 97.688% while the rest of the optimization algorithms performed \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**A: CAR** & & & & \\ \hline
**Algorithm** & **Time (s)** & **Best Pos** & **Best Sol (\%)** & **Iter** \\ \hline PSO & 14.02 & [174; 6] & 97.399 & 5 \\ GA & 27.65 & [163; 6] & 97.399 & 6 \\ BBO & 38.5 & [120; 6] & 97.399 & 2 \\ BO & 79.47 & [129; 6] & 97.688 & 1 \\ SDSGA & 22.48 & [117; 6] & 97.688 & 3 \\ \hline \hline
**B: BREAST CANCER** & & & & \\ \hline
**Algorithm** & **Time (s)** & **Best Pos** & **Best Sol (\%)** & **Iter** \\ \hline PSO & 60.43 & [129; 7] & 98.246 & 3 \\ GA & 47.95 & [143; 7] & 98.246 & 7 \\ BBO & 62.92 & [118; 16] & 98.246 & 4 \\ BO & 106.4 & [68; 20] & 98.246 & 5 \\ SDSGA & 41.37 & [124; 7] & 98.246 & 2 \\ \hline \hline
**C: LETTER** & & & & \\ \hline
**Algorithm** & **Time (s)** & **Best Pos** & **Best Sol (\%)** & **Iter** \\ \hline PSO & 787.68 & [198; 5] & 96.45 & 3 \\ GA & 427.81 & [141; 5] & 96.45 & 5 \\ BBO & 599.96 & [91; 4] & 96.5 & 7 \\ BO & 616.72 & [141; 5] & 96.45 & 10 \\ SDSGA & 422.75 & [85; 4] & 96.5 & 2 \\ \hline
**D: ADULT** & & & & \\ \hline
**Algorithm** & **Time (s)** & **Best Pos** & **Best Sol (\%)** & **Iter** \\ \hline PSO & 763.43 & [184; 15] & 85.86 & 4 \\ GA & 780 & [138; 15] & 85.79 & 3 \\ BBO & 953.09 & [193; 15] & 85.87 & 8 \\ BO & 942.51 & [183; 15] & 85.843 & 4 \\ SDSGA & 655.41 & [184; 15] & 85.86 & 7 \\ \hline
**E: Cover Type** & & & & \\ \hline
**Algorithm** & **Time (s)** & **Best Pos** & **Best Sol (\%)** & **Iter** \\ \hline PSO & 11,847.65 & [198; 19] & 93.1 & 8 \\ GA & 5481.84 & [155; 18] & 93.081 & 6 \\ BBO & 11,721.25 & [193; 18] & 93.092 & 7 \\ BO & 13,437.37 & [188; 20] & 93.133 & 3 \\ SDSGA & 10,907.01 & [194; 20] & 93.129 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the metaheuristic-based algorithms on RF trained on four different UCI datasets. the same at a slightly lower value of 97.399%. In the LETTER dataset, SDSGA and BBO performed the best at 96.5%, whereas the BO, GA and PSO algorithms performed equally at 96.45%. The BBO algorithm performed better than the other algorithms in the ADULT dataset at 85.87%, whereas the PSO and SDSGA algorithms were tied at 85.86%. The BO algorithm performed at 85.843%, while GA performed the worst at 85.79%. The COVER TYPE dataset had the largest variation in accuracy with BO performing the best at 93.133%, whereas the SDSGA performed at 93.129%.