## 6 Comparison of PSO, BA, and DE Regarding Data Classification
### NN Classifications with BA-Based Initialization Approaches  
The multi-layer feed forward neural network is trained with the back propagation algorithm, standard BA, BA-SO, BA-H, and proposed BA-TO, BA-KN, and BA-WE. The comparison of these training approaches is tested on real classification problem datasets taken from the UCI repository. The cross validation method used to compare the performances of different classification techniques. In the paper, the k-fold cross validation method used for comparison of classification performances for the training of the neural network with back propagation, standard BA, BA-SO, BA-H, and proposed BA-TO, BAKN, and BAWE is used. The k-fold cross validation method was proposed and used in the experimental with value k = 10. The dataset is divided into 10 chunks where each chunk of data contains the same proportion of each class of dataset. One chunk is used as testing while nine chunks are used as training phase. The experimental results of algorithms such as with back propagation, standard BA, BA-SO, BA-H, and proposed BA-TO, BA-KN, and BA-WE are compared with each other on seven well-known real datasets taken from UCI and their performances are evaluated. In Table 15, the simulation results show that training of the neural network with the BA-KN algorithm outperforms in accuracy and is capable of providing good classification accuracy than the other traditional approaches. The BA-WE  
Figure 59: Box plot visualization of the results achieved by the training of FFNN for all DE-based initialization approaches and BPA for given datasets of classification problem.  
Figure 60: Multi-comparison post-hoc [PERSON] test graph of all DE-based.  
algorithm may be used effectively for data classification and statistical problem in the future as well. Figure 61 represents the accuracy graph for seven datasets.  
For giving the evidence of simulation results, the results of seven variants of BA initialization obtained from classification were examined by the statistical tests such as one-way ANOVA and post-hoc Tukey tests [60] for pair-wise likeliness (pair-wise comparisons) under the condition of 0.05 significance level. The outcomes of the one-way ANOVA test are presented in Table 16, which show the significance level less than 0.05 is 0.03623. The outcomes of Table 16 revealed that there is significant divergence in all initialization variants of BA with a 95% confidence level. Figure 62 displays the graph of one-way ANOVA which gives proof that KN-BANN is significantly superior to the other initialization variants of BANN. Figure 63 displays the outcomes of pair-wise likeliness comparisons (pair-wise comparisons) of initialization variants of BANN by using post-hoc [PERSON]'s statistical test. The plotted graph shows that the KN-BANN initialization variant is statistically divergent than other initialization variants of BANN having a 95% confidence level.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Relation** & **Sum of Squares** & **df** & **Mean Square** & **F** & **Significance** \\ \hline Testing Accuracy & Among groups & 845.8 & 6 & 140.967 & 2.5113 & 0.03623 \\ \hline \hline \end{tabular}
\end{table}
Table 16: One-way ANOVA results of BA variants.  
Figure 61: Classification testing accuracy results.