## 5 Experiments and Results
### Effect of Swarm Size  
It has been long argued that population-based metaheuristic algorithms can achieve reasonable good results compared to other search algorithms if they have a good-enough large population size with enough time. However, the swarm or the population size parameter has a considerable influence on the performance of evolutionary algorithms whether in promoting or degrading it. This experiment is conducted to study and substantiate the effect of the swarm size on the performance of the proposed SSA-KELM. Hence, the swarm size of the SSA has been investigated at different values; 50, 60, 70, 80, 90, and 100, as discussed in Tables 4 and 5. Table 4 illustrates the effect of the population size on the features reduction rate. It is clear from the table that increasing the population size not necessarily results in a better reduction rate. To illustrate, when the population size is 50, the obtained average number of features was 220.3 which yields nearly a 30% reduction in the features. However, at the swarm size of 100, it is obvious that the number of features dramatically decreased with the best-accomplished reduction rate that is 52%. On the other hand, when the swarm size was 60, 70, 80, and 90, the average number of features was higher than what is obtained by the swarm size of 100. For example, at the swarm size of (80), the average number of features was 172.3 and the reduction rate was 49.5%. Even that at all experimented swarm sizes, the SSA algorithm has decreased the number of features relatively efficiently that justified with a prominent performance results as represented by Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Population Size** & **Reduction Rate** & **Average No. of Features** \\ \hline
50 & 29.9\% & 220.3 \\
60 & 46.7\% & 167.3 \\
70 & 51.0\% & 153.8 \\
80 & 45.1\% & 172.3 \\
90 & 49.5\% & 158.7 \\
100 & **52.0\%** & **150.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The influence of several population sizes of the SSA algorithm in terms of reduction rate and average number of features. Figure 9: Convergence curves of the SSA algorithm at different ratios of leaders. Table 5 compares the average performance of the SSA algorithm at different population sizes regarding the accuracy, recall, FPR, TNR, precision, g-mean, and AUC with the average standard deviation values. It is obvious from the table that the superior performance obtained when the swarm size was 50. At which, the accuracy, TNR, precision, g-mean, and AUC were 98%, while the FPR was the minimal that accounted for 0.020 with standard deviation (0.028). Examining the accuracy results, when the swarm size is greater than 50, the algorithms achieved high results with the slight difference among all, where they accomplished 97.3%, 97.0%, 96.7%, 97.5%, and 97.4%, in ascending order. Regarding the recall, even that the SSA algorithm at the size (50) achieved 98%, but at sizes (90, 100) the SSA algorithm could perform 98.2%, while in contrast, the SSA at (90, 100) performed worst in terms of FPR. For instance, when the size=90, the FPR=0.032, and when it was 100, the FPR=0.034, however, at 50, the FPR=0.020. Nonetheless, regarding the TNR, the SSA at (50, 60, 70, 80, 90, and 100) achieved 98%, 96.8%, 97%, 96%, 96.8%, and 96.6%, respectively. Similarly, at the precision, at 50, the algorithm achieved the best (98%), whereas, at 80, it achieved the minimal precision (96.1%). Additionally, at both g-mean and AUC, the best performance attained at swarm size of (50) with a performance of (98%) and with feasible standard deviation, however, (80) achieved the minimum, which is (96.7%). To sum up, the SSA algorithm accomplished striking results with a considerable feature reduction ratio. To support the superiority of the proposed SSA-KELM, it has been compared with well-known classical machine learning algorithms as given by the following sub-section. Figure 10 describes the error rate at various swarm sizes of SSA.