## 5 IRSA for Solving Classification Problems  
In order to evaluate the performance of the IRSA, eight datasets obtained from [55; 56] were used. Table 6 gives a brief description of the datasets. Each dataset was divided into a training set and a testing set. Almost 67% of the data was used for training the ANN, and the remaining 33% was used for testing. Sigmoid was used as an activation function for the hidden layers. Normalized mean squared error, as described by Equation (27), was used as a cost function.  
\[NRMSE=\frac{1}{\tilde{T}}\sqrt{\frac{1}{N}\sum_{i=1}^{N}(T_{i}-P_{i})^{2}} \tag{27}\]  
where \(T_{i}\) and \(p_{i}\) are the true and predicted values. \(\tilde{T}\) represents the mean of true value. \(N\) represents the total number of data samples. As there is no fixed rule to select the number of neurons, we selected neurons based on the formula [37]:  
\[h=2\times f+1 \tag{28}\]  
where \(f\) represents the number of input features, and \(h\) shows the number of selected neurons.  
After training the MLP network, different convergence curves for IRSA, RSA, BMO, AOA, and PSO are displayed in Figure 8. The convergence of IRSA is at par with all the other metaheuristic algorithms. On the other hand, the convergence of the classical RSA was average. The accuracy while using the IRSA algorithm was better than the accuracy of other algorithms. The IRSA provided the best results for most of the datasets. Additionally, the low standard deviation of the IRSA was an indication of its strength and stability. The results in Table 7 clearly show that the accuracy of the proposed method is higher than the other four classifiers. Table 8 shows the performance of the comparative techniques in testing the dataset, and Table 9 shows the cost function comparison of all the techniques with best, average, and STD values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & **\# Classes** & **\# Features** & \
\begin{tabular}{c} **No. of Training** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **No. of Testing** \\ **Samples** \\ \end{tabular} \\ \hline Iris & 3 & 4 & 100 & 50 \\ Heart & 2 & 13 & 203 & 100 \\ Stress-Lysis & 3 & 3 & 1340 & 661 \\ Banknote-authentication & 2 & 4 & 919 & 453 \\ Blood-transfusion & 2 & 4 & 501 & 247 \\ Cryotherapy & 2 & 6 & 60 & 30 \\ Diabetes & 2 & 8 & 514 & 254 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Description of used dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Training Accuracy (\%)**} \\ \cline{2-5}  & **PSONN** & **BMOANN** & **AOANN** & **RSANN** & **IRSANN** \\ \hline Iris & 97 & 94 & 93 & 88.02 & 97 \\ Heart & 72.60726 & 66.9967 & 59.73597 & 42.24422 & 88 \\ Stress-Lysis & 91.75412 & 95.8021 & 70.16492 & 59.37031 & 99.42171 \\ Banknote-authentication & 94.42623 & 97.9235 & 85.68306 & 84.48087 & 99.45355 \\ Blood-transfusion & 79.95992 & 78.15631 & 77.35471 & 75.1503 & 77.55511 \\ Cryotherapy & 90 & 92 & 71.66667 & 81.66667 & 96.66667 \\ Diabetes & 74.80469 & 75 & 72.07031 & 66.79688 & 78.10156 \\ Haberman & 75 & 75 & 78.43137 & 79.41176 & 78.43137 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training accuracy.  
Figure 8: Convergence curves for classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Testing Accuracy (\%)**} \\ \cline{2-5}  & **PSONN** & **BMOANN** & **AOANN** & **RSANN** & **IRSANN** \\ \hline Iris & 94 & 98.66 & 92 & 91 & 98.23 \\ Heart & 70.9571 & 75.90759 & 58.08581 & 46.53465 & 85 \\ Stress-Lysis & 91.90405 & 97.0015 & 70.01499 & 57.12144 & 99.7121 \\ Banknote-authentication & 93.43545 & 96.49891 & 83.3698 & 85.33917 & 99.56236 \\ Blood-transfusion & 77.51004 & 77.91165 & 74.6988 & 79.11647 & 80.72289 \\ Cryotherapy & 93.33333 & 86.66667 & 80 & 76.66667 & 93.33333 \\ Diabetes & 67.57813 & 75.78125 & 76.1718867 & 69.92188 & 77.26563 \\ Haberman & 67.47059 & 76.47059 & 76.64706 & 70.09804 & 80.54902 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Testing accuracy.