## 6 Results and Discussion
### Comparison between Various Versions of BHHO with Basic and Time Varying V-Shaped TFs  
In general, experimental results show that HHO with V-shaped time-varying transfer functions (TV-TFs) is better compared to those with classic V-shaped TFs. Inspecting the results in Table 4, in the case of BHHO\({}_{V1}\) and BHHO\({}_{TV1}\), BHHO\({}_{V1}\) has recorded higher accuracy rates on seven datasets while BHHO\({}_{TV1}\) has found higher accuracy rates \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **No. of Features** & **No. of Instances** & **No. of Classes** \\ \hline KrvskpEW & 36 & 3196 & 2 \\ Tic-tac-toe & 9 & 958 & 2 \\ Vote & 16 & 300 & 2 \\ WaveformEW & 40 & 5000 & 3 \\ WineEW & 166 & 476 & 3 \\ Zoo & 16 & 101 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline  & **Common Parameters** \\ \hline Number of runs & 30 \\ population size & 10 \\ Number of iterations & 100 \\ Dimension & \#features \\ Fitness function & \(\alpha=0.99\), \(\beta=0.01\) \\ K for KNN classifier & 5 \\ \hline \hline \multicolumn{3}{c}{**Internal Parameters**} \\ \hline GSA & \(G_{0}=10\) \\  & \(c_{1}=c_{2}=2\) \\ PSO & \(\omega\): from 0.9 to 0.2 \\ BA & \(Q_{min}=0\), \(Q_{max}=2\) \\  & \(A\) loudness = 0.5, \(r\) Pulse rate = 0.5 \\ WOA & \(a\): from 2 to 0 \\  & \(a_{2}\): from \(-1\) to \(-2\) \\ HHO & \(E\): from 2 to 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Common and internal parameters used in the experiments. for eight cases. However, both approaches have the same accuracy rates in three cases. In addition, we see that BHHO\({}_{TV2}\) has better accuracy measures than BHHO\({}_{V2}\) on eleven datasets, whereas BHHO\({}_{V2}\) outperforms BHHO\({}_{TV2}\) in five cases. It can be observed that BHHO\({}_{TV2}\) and BHHO\({}_{V2}\) have maximum accuracy rates in two cases (M-of-N and Zoo). In the case of BHHO\({}_{V3}\) and BHHO\({}_{TV3}\), it can be noticed that BHHO\({}_{TV3}\) outperforms BHHO\({}_{V3}\) on nine datasets while BHHO\({}_{V3}\) obtained higher accuracy rates on five datasets. It can be seen that both approaches obtained similar accuracy rates on the exactly dataset and the maximum accuracy measures on three datasets, including M-of-N, WineEW, and Zoo. As per results, BHHO\({}_{TV4}\) outperforms BHHO\({}_{V4}\) on eleven datasets in terms of accuracy rates, whereas BHHO\({}_{V4}\) is superior in only three cases. However, both methods obtained similar maximum obtained maximum accuracy rates on four datasets. In terms of classification accuracy, as per F-test results, it can be seen that BHHO\({}_{TV4}\) is ranked as the best, followed by the BHHO\({}_{TV3}\) method. Based on the observed results, we can say that HHO with TV4 transfer function is able to obtain the best classification accuracy compared to its peers, including basic and time-varying TFs-based FS approaches. In terms of selected features, as presented in Table 5, it can be seen that the basic versions of V1 and V2 based approaches outperform the time-varying-based ones. In the case of BHHO\({}_{V3}\) and BHHO\({}_{TV3}\), it is clear that BHHO\({}_{TV3}\) is dominant on 61.11% of cases while BHHO\({}_{TV4}\) outperformed BHHO\({}_{TV4}\) on 50% of the cases. According to recorded FS rates, F-test results show that BHHO\({}_{V4}\) is ranked as the best method in terms of the least number of selected features. However, excessive feature reduction may not be the preferred option since it may exclude some relevant features, which degrade the classification performance. Although the basic versions of TFs-based approaches outperform the time-varying-based ones in terms of feature reduction, the latter can find the most relevant subset of features that provides better classification accuracy, as provided in Table 4.  
To confirm the effectiveness of the competing algorithms, the fitness value that combines the two measures (i.e., accuracy and reduction rate) is adopted.