## 5 Results and Discussion of Findings
### Performance Evaluation of State-of-the-Art Classifiers on Methods
The graph plots for BDMO and BIBOSA demonstrate another interesting aspect of their performance, i.e., all classifiers in each case peaked and deepened with a population size of 50. For instance, for BDMO, all the classifiers peaked with a population size of 75 with classification accuracies 0.781, 0.80, 0.801, 0.822 and 0.82 for KNN, RF, MLP, DT, SVM and GNB, respectively. BIBOSA yielded the best classification accuracies for all classifiers with a population size of 25, showing values just above 0.925 for KNN, around 0.950 for MLP, DT and SVM, and around 0.975 for RF and GNB. BSNDO obtained curves running consistently at 0.805 for RF, MLP, DT, SVM and GNB, but obtained approximately 0.76 for all population sizes using the KNN classifier. With BPSO and BEOSA, KNN peaked with a population size of 100 and 75 at 0.989 and 0.0650 accuracies, while GNB peaked at 0.95 and around 0.9540 with a population size of 50 for BPSO and BEOSA. SVM obtained its peak performance at values of 0.959 and 0.9575 on BPSO and BEOSA with population sizes of 100 and 50. With BPSO and BEOSA, MLP peaked with population sizes of 100 and 75 at 0.96 and around 0.9525, while RF peaked at 0.959 and 0.9575 with population sizes of 50 and 75 for BPSO and BEOSA. DT peaked with a similar accuracy to that reported for GNB. Figure 9 shows the performance of the BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIBOSA algorithms with the SpectEW dataset, providing the classification accuracies of the KNN, RF, MLP, DT, SVM, and GNB classifiers. From the plots shown in the figure, it can be seen that the best classification accuracies were obtained with population sizes of 25, 50, 75, and 100 for BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIBOSA using SVM, KNN, SVM (also DT and KNN), MLP, RF, KNN, KNN and RF. The result also showed that for BPSO, BSFO and BIBOSA, the best performance of their respective classifiers was obtained with a population size of 100. In contrast, BSFO, BDMO, BSNDO and BEOSA obtained their best classification accuracy with a population size of 25 using their respective classifiers. We note that BSFO and BGWO also performed well with a population size of 75, while BWOA did well with a population size of 50. Figure 8: Conf. Figure 8: Classification accuracy of the KNN, RE, MLP, decision tree, SVM, and Naive Bayes models with population sizes of 25, 50, 75, and 100 using the (a) BWOA, (b) BPSO, (c) BSFO, (d) BGWO, (e) BDMO, (f) BENDO, (g) BESOA, and (h) BEGSA algorithms with the CongressEW dataset. Figure 9: Conf. Figure 9: Classification accuracy of the KNN, RF, MLP, decision tree, SVM and Naive Bayes models with population sizes of \(25,50,75,\) and \(100\), using the (a) BWOA, (b) BPSO, (c) BSFO, (d) BCWO, (e) BDMO, (f) BSNDO, (g) BCSA and (h) BIBOSA algorithms with the SpectEV dataset. The performance of BWOA with the SpectEW dataset showed that most classifiers achieved their peak accuracies with a population size of 50, except for GNB, which obtained its best output with a population size of 100, albeit at a much lower value of 0.64. Meanwhile, KNN, RF, MLP, DT and SVM obtained values of 0.87, 0.84, 0.84, 0.79 and 0.89, respectively. BPSO showed an interesting result when a population size of 50 was used, with all classifiers converging at a classification accuracy of 0.79 as their lowest values. Interestingly, their best accuracies also occurred with a population size of 100, with KNN yielding 0.89, RF 0.83, MLP 0.84, DT 0.80, SVM0.79 and GNB 0.73. The BSFO algorithm is unique, as it showed recurring overlap with most of the classifiers, as can be seen with GNB and DT, whose maximum accuracy values were 0.75 with a population size of 75, while others also peaked at that point but with a classification accuracy of 0.8. Meanwhile, differentiated classification accuracies were observed for all classifiers when using the BGWO algorithm. The RF and GNB classifiers obtained their best performance with a population size of 50, i.e., 0.83 and 0.7.