# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Optimizer**} & \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**IGD**} & \multicolumn{2}{c}{**HV**} & \multicolumn{2}{c}{**Spacing**} & \multicolumn{2}{c}{**Spread**} \\ \cline{3-10}  & & & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** \\ \hline \multirow{8}{*}{MLP} & \multirow{3}{*}{MOEA/D} & SLI & 99.378 & 12.424 & 1.455 & 0.462 & 0.802 & 1.564 & 1.298 & 0.447 \\  & & SVD & 97.978 & 12.900 & 4.019 & 0.825 & 1.444 & 2.072 & 1.530 & 0.491 \\  & & VOICED & 96.312 & 13.059 & 8.459 & 1.769 & 1.556 & 3.275 & 1.266 & 0.441 \\ \cline{2-10}  & \multirow{3}{*}{SPEA-II} & SLI & **19.096** & 9.635 & 3.880 & 0.432 & **0.659** & 0.855 & **1.685** & 0.368 \\  & & SVD & **23.348** & 9.737 & **14.340** & 2.088 & **0.689** & 0.813 & **1.858** & 0.241 \\  & & VOICED & 31.076 & 11.602 & **23.985** & 4.138 & **0.269** & 0.338 & 1.593 & 0.421 \\ \cline{2-10}  & \multirow{3}{*}{NSGA-II} & SLI & 22.941 & 10.620 & **4.049** & 0.668 & 1.060 & 1.617 & 1.676 & 0.549 \\ \cline{1-1}  & & SVD & 25.337 & 9.551 & 14.044 & 1.372 & 0.712 & 0.429 & 1.820 & 0.298 \\ \cline{1-1}  & & VOICED & **30.488** & 13.773 & 22.070 & 2.879 & 1.048 & 1.891 & **1.765** & 0.310 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimization results of NSGA-II, MOEA/D, and SPEA-II with MLP classifier across three datasets (SLI, SVD, and VOICED). The best results were highlighted with boldface style. Figure 4 depicts the approximated Pareto-front for the three datasets from all optimizers and all classifiers. Figure 4A-C present the best obtained Pareto-front from the NSGA-II for SVD, VOICED, and SLI, respectively, among all classifiers. Figure 4D shows the actual Pareto-front (the non-dominated solutions) obtained by all classifiers over all runs. Table 9 shows the required optimization time (i.e., training time) of the NSGA-II optimizer across KNN, MLP, RF, and SVM applied over the three datasets, with the average and standard deviation values computed. The optimization time represents the needed time for the optimization phase of the evolutionary algorithm to minimize the number of features  
Figure 4: Comparison of the best Pareto-Front per dataset and overall datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Optimizer**} & \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**IGD**} & \multicolumn{2}{c}{**HV**} & \multicolumn{2}{c}{**Spacing**} & \multicolumn{2}{c}{**Spread**} \\ \cline{3-11}  & & & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** \\ \hline \multirow{8}{*}{SVM} & \multirow{3}{*}{MOEA/D} & SLI & 99.978 & 13.368 & 3.092 & 0.967 & 1.477 & 1.747 & **1.920** & 0.246 \\  & & SVD & 104.645 & 8.046 & 1.644 & 0.462 & 1.472 & 1.461 & **1.912** & 0.246 \\  & & VOICED & 90.378 & 10.354 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \cline{2-11}  & \multirow{3}{*}{SPEA-II} & SLI & 23.230 & 8.986 & 8.035 & 1.178 & **0.726** & 0.990 & 1.297 & 0.574 \\ \cline{2-11}  & & SVD & **30.228** & 12.511 & **7.797** & 1.090 & **1.369** & 1.120 & 1.771 & 0.169 \\ \cline{2-11}  & & VOICED & 72.978 & 9.934 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \cline{2-11}  & \multirow{3}{*}{NSGA-II} & SLI & **22.000** & 9.565 & **9.392** & 1.450 & 1.327 & 1.060 & 1.796 & 0.141 \\ \cline{2-11}  & & SVD & 35.040 & 8.447 & 1.766 & 0.181 & 1.950 & 1.190 & 1.809 & 0.200 \\ \cline{2-11}  & & VOICED & **24.059** & 12.257 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimization results of NSGA-II, MOEA/D, and SPEA-II with SVM classifier across three datasets (SLI, SVD, and VOICED). The best results were highlighted with a boldface style. and error rate. The results indicate significant variations in optimization time depending on the classifier and dataset.