## 6 Results and Discussion
### Comparison with the State of the Art Approaches
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**ID** & & **bWSO-log** & **bAAA** & **bBAT** & **bFA** & **bGWO** & **bMFO** & **bMVO** & **bPSO** & **bWOA** \\ \hline \multirow{4}{*}{N1} & \(\uparrow\)mean & **0.960** & 0.950 & 0.948 & 0.954 & 0.959 & 0.958 & 0.952 & 0.958 & 0.952 \\  & \(\pm\)std & 0.017 & 0.023 & 0.023 & 0.018 & 0.018 & 0.020 & 0.020 & **0.016** & 0.019 \\  & \(\uparrow\)best & 0.986 & **0.993** & 0.979 & 0.986 & **0.993** & **0.993** & 0.986 & 0.986 & 0.986 \\  & \(\uparrow\)worst & 0.921 & 0.900 & 0.886 & 0.907 & 0.914 & 0.921 & 0.914 & **0.929** & 0.893 \\ \hline \multirow{4}{*}{N2} & \(\uparrow\)mean & **0.661** & 0.628 & 0.648 & 0.633 & 0.635 & 0.637 & 0.630 & 0.627 & 0.633 \\  & \(\pm\)std & 0.056 & 0.073 & 0.069 & 0.056 & 0.069 & 0.066 & **0.054** & 0.079 & 0.073 \\  & \(\uparrow\)best & 0.757 & **0.784** & 0.757 & 0.757 & 0.757 & 0.730 & **0.784** & 0.757 \\  & \(\uparrow\)worst & **0.541** & 0.459 & 0.486 & 0.514 & 0.514 & 0.514 & 0.514 & 0.486 & 0.486 \\ \hline \multirow{4}{*}{N3} & \(\uparrow\)mean & **0.841** & 0.833 & 0.636 & 0.785 & 0.831 & 0.829 & 0.821 & 0.691 & 0.818 \\  & \(\pm\)std & 0.050 & 0.051 & 0.056 & 0.079 & **0.046** & 0.064 & 0.081 & 0.111 & 0.085 \\  & \(\uparrow\)best & 0.917 & 0.917 & 0.717 & 0.900 & 0.917 & **0.950** & 0.933 & 0.883 & **0.950** \\  & \(\uparrow\)worst & 0.733 & 0.700 & 0.467 & 0.550 & **0.750** & 0.717 & 0.583 & 0.500 & 0.633 \\ \hline \multirow{4}{*}{N4} & \(\uparrow\)mean & 0.912 & **0.924** & 0.705 & 0.908 & 0.869 & **0.924** & 0.902 & 0.767 & 0.879 \\  & \(\pm\)std & 0.057 & **0.042** & 0.100 & 0.064 & 0.102 & 0.048 & 0.052 & 0.109 & 0.048 \\  & \(\uparrow\)best & **1.000** & **1.000** & 0.972 & **1.000** & 0.972 & 0.972 & **1.000** & 0.972 & 0.972 \\  & \(\uparrow\)worst & 0.750 & **0.806** & 0.472 & 0.694 & 0.528 & **0.806** & 0.750 & 0.556 & 0.778 \\ \hline \multirow{4}{*}{N5} & \(\uparrow\)mean & 0.856 & 0.860 & 0.851 & 0.843 & **0.889** & 0.844 & 0.832 & 0.856 & 0.846 \\  & \(\pm\)std & 0.085 & 0.094 & **0.069** & 0.135 & 0.078 & 0.093 & 0.104 & 0.082 & 0.088 \\  & \(\uparrow\)best & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & 0.952 & 0.952 \\  & \(\uparrow\)worst & 0.619 & 0.571 & **0.667** & 0.429 & 0.619 & 0.619 & 0.571 & **0.667** & 0.571 \\ \hline \multirow{4}{*}{N6} & \(\uparrow\)mean & **0.780** & 0.762 & 0.704 & 0.738 & 0.749 & 0.773 & 0.758 & 0.748 & 0.752 \\  & \(\pm\)std & **0.062** & 0.078 & 0.077 & 0.077 & 0.065 & 0.080 & 0.081 & 0.097 & 0.087 \\  & \(\uparrow\)best & 0.867 & 0.933 & 0.867 & 0.867 & 0.900 & **1.000** & 0.933 & 0.900 & 0.900 \\  & \(\uparrow\)worst & 0.600 & **0.633** & 0.500 & 0.533 & 0.600 & **0.633** & 0.567 & 0.467 & 0.500 \\  & \(\uparrow\)mean & **0.940** & 0.931 & 0.915 & 0.920 & 0.923 & 0.926 & 0.927 & 0.919 & 0.920 \\  & \(\pm\)std & 0.020 & 0.019 & 0.023 & 0.027 & 0.027 & 0.028 & **0.017** & 0.028 & 0.032 \\  & \(\uparrow\)best & 0.972 & 0.963 & 0.954 & 0.963 & 0.963 & **0.991** & 0.954 & 0.981 & 0.963 \\  & \(\uparrow\)worst & **0.907** & 0.889 & 0.870 & 0.852 & 0.861 & 0.870 & 0.889 & 0.861 & 0.815 \\ \hline \multirow{4}{*}{N8} & \(\uparrow\)mean & **0.822** & 0.795 & 0.760 & 0.804 & 0.783 & 0.815 & 0.780 & 0.790 & 0.792 \\  & \(\pm\)std & **0.057** & 0.070 & 0.059 & 0.057 & 0.070 & 0.083 & 0.061 & 0.069 & 0.068 \\  & \(\uparrow\)best & 0.935 & 0.903 & 0.839 & 0.935 & 0.935 & **0.968** & 0.903 & 0.903 & 0.935 \\  & \(\uparrow\)worst & **0.742** & 0.613 & 0.645 & 0.677 & 0.677 & 0.645 & 0.645 & 0.645 & 0.677 \\ \hline \multirow{4}{*}{N9} & \(\uparrow\)mean & **0.820** & 0.815 & 0.668 & 0.817 & 0.798 & 0.816 & 0.812 & 0.809 & 0.815 \\  & \(\pm\)std & **0.010** & 0.011 & 0.055 & 0.011 & 0.014 & 0.014 & 0.014 & 0.016 & 0.013 \\ \cline{1-1}  & \(\uparrow\)best & 0.845 & 0.840 & 0.770 & **0.846** & 0.835 & 0.842 & 0.837 & 0.841 & 0.838 \\ \cline{1-1}  & \(\uparrow\)worst & **0.802** & 0.794 & 0.503 & 0.792 & 0.770 & 0.790 & 0.790 & 0.778 & 0.789 \\ \hline \multirow{4}{*}{N10} & \(\uparrow\)mean & 0.817 & **0.861** & 0.842 & 0 Figure 5 presents a comparative analysis of the convergence performance of the proposed bWSO-log algorithm against other algorithms for the N1, N5, N7, and N10 datasets.