# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Comparison of PSO Variant Algorithms for Benchmark Functions
#### 4.2.2 Numerical Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multicolumn{2}{c}{**MMPSO (Ours)**} & \multicolumn{2}{c}{**HIDMSPSO**} & \multicolumn{2}{c}{**HCLPSO**} & \multicolumn{2}{c}{**CLPSO**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} \\ \hline F01 & \(5.11\times 10^{-14}\) & \(1.79\times 10^{-14}\) & \(7.96\times 10^{-14}\) & \(3.11\times 10^{-14}\) & \(8.43\times 10^{-14}\) & \(2.87\times 10^{-14}\) & \(5.49\times 10^{-14}\) & \(1.13\times 10^{-14}\) \\ F02 & \(2.96\times 10^{-12}\) & \(4.59\times 10^{-12}\) & \(1.79\times 10^{-10}\) & \(1.31\times 10^{-10}\) & \(8.23\times 10^{-5}\) & \(1.10\times 10^{-4}\) & \(1.09\times 10^{3}\) & \(2.81\times 10^{2}\) \\ F03 & \(3.97\times 10^{5}\) & \(2.24\times 10^{5}\) & \(4.77\times 10^{5}\) & \(2.38\times 10^{5}\) & \(9.41\times 10^{5}\) & \(5.40\times 10^{5}\) & \(1.28\times 10^{7}\) & \(4.05\times 10^{6}\) \\ F04 & \(\mathbf{21.98}\) & \(\mathbf{24.53}\) & \(67.49\) & \(54.41\) & \(6.65\times 10^{2}\) & \(3.40\times 10^{2}\) & \(8.59\times 10^{3}\) & \(1.85\times 10^{3}\) \\ F05 & \(2.12\times 10^{3}\) & \(4.98\times 10^{2}\) & \(\mathbf{5.89\times 10^{2}}\) & \(\mathbf{4.69\times 10^{2}}\) & \(3.28\times 10^{3}\) & \(6.14\times 10^{2}\) & \(4.41\times 10^{3}\) & \(3.99\times 10^{2}\) \\ F06 & \(\mathbf{0.42}\) & \(\mathbf{1.25}\) & \(64.56\) & \(51.18\) & \(2.48\) & \(2.06\) & \(1.04\) & \(1.64\) \\ F07 & \(9.61\times 10^{-3}\) & \(\mathbf{8.87\times 10^{-3}}\) & \(1.33\times 10^{-2}\) & \(1.50\times 10^{-2}\) & \(1.91\times 10^{-2}\) & \(1.51\times 10^{-2}\) & \(0.75\) & \(0.12\) \\ F08 & \(\mathbf{20.52}\) & \(\mathbf{6.99\times 10^{-2}}\) & \(20.61\) & \(0.12\) & \(20.80\) & \(6.70\times 10^{-2}\) & \(20.89\) & \(7.08\times 10^{-2}\) \\ F09 & \(18.39\) & \(7.43\) & \(40.62\) & \(9.34\) & \(1.10\times 10^{-13}\) & \(3.30\times 10^{-14}\) & \(\mathbf{5.68\times 10^{-14}}\) & \(0\) \\ F10 & \(\mathbf{27.95}\) & \(\mathbf{5.65}\) & \(45.79\) & \(14.15\) & \(56.19\) & \(14.22\) & \(1.13\times 10^{2}\) & \(16.75\) \\ F11 & \(\mathbf{8.48}\) & \(2.65\) & \(15.89\) & \(5.18\) & \(20.69\) & \(2.94\) & \(24.57\) & \(1.64\) \\ F12 & \(8.16\times 10^{3}\) & \(4.38\times 10^{3}\) & \(6.61\times 10^{3}\) & \(6.99\times 10^{3}\) & \(4.78\times 10^{3}\) & \(4.57\times 10^{3}\) & \(1.35\times 10^{4}\) & \(4.09\times 10^{3}\) \\ F13 & \(1.95\) & \(0.27\) & \(3.68\) & \(0.98\) & \(\mathbf{1.48}\) & \(0.22\) & \(1.82\) & \(\mathbf{0.19}\) \\ F14 & \(\mathbf{11.09}\) & \(0.50\) & \(11.74\) & \(0.49\) & \(12.36\) & \(0.88\) & \(12.64\) & \(\mathbf{0.21}\) \\ F15 & \(3.44\times 10^{2}\) & \(1.38\times 10^{2}\) & \(3.66\times 10^{2}\) & \(84.25\) & \(89.39\) & \(87.76\) & \(\mathbf{37.57}\) & \(\mathbf{20.42}\) \\ F16 & \(96.69\) & \(40.84\) & \(1.51\times 10^{2}\) & \(1.66\times 10^{2}\) & \(1.08\times 10^{2}\) & \(37.83\) & \(1.57\times 10^{2}\) & \(22.33\) \\ F17 & \(\mathbf{1.13\times 10^{2}}\) & \(\mathbf{1.06\times 10^{2}}\) & \(1.16\times 10^{2}\) & \(1.12\times 10^{2}\) & \(1.28\times 10^{2}\) & \(49.22\)