## 5 Results and Discussion of Findings
### Comparative Analysis of the Classification Accuracy of All Methods
The plots for classification curve analysis are presented in Figure 7. The curves of all the methods on the Zoo dataset showed that BEOSA and BIEOSA performed better than any of the other methods. Similarly, we observed that the BEOSA method performed well on Vote, Wine, Sonar, Tic-tac-toe, HeartEW, CongressEW, BreastEW and SpectEW datasets. Using the M-of-n, Ionosphere, Exactly, and Exactly2 datasets, the BEOSA and BIEOSA methods demonstrated strong competition with the BPSO method while outperforming the remaining methods. Figure 7: _Cont._  
Figure 7: Graph-based comparative analysis of the classification accuracy performance for all binary optimization methods on (**a**) Zoo; (**b**) Vote; (**c**) Wine; (**d**) Sonar; (**e**) Tic-tac-toe; (**f**) M-of-**n**; (**g**) Ionosphere; (**h**) Exactly; (**i**) Exactly2; (**j**) HeatEW; (**k**) CongressEW; (**l**) Lymphography; (**m**) Colon, (**n**) BreastEW; and (**o**) SpectEW datasets. The classification accuracy for the Zoo dataset on each of the algorithms shows that the BDMO curve runs from 0.59 and ends at 0.61 with a peak at 0.66. BSNDO had a straight curve on 0.65. BIEOSA started from 0.75, peaked at 0.79 and ended at 0.78, BWOA started from 0.87, deep at 078 and ended at the same 0.78, BGWO started from 0.90 peaked and dipped at 0.85 and 0.89 respectively, and ended at 0.86. BPSO and BEOSA top the plots with their curves starting from 0.91 and 0.92 and ending at 0.88 and 0.91, respectively. For the Vote dataset, BDMO rose from 0.775 to 0.825, and BIEOSA started from 0.840, peaked at 0.875 and ended below 0.825. BGWO rose from above 0.850 and terminated above 0.875, while BWOA started from 0.875 and rose slightly to 0.880. BPSO and BSNDO both started just above 0.925 and ended at 0.935 and 0.925, respectively. BEOSA tops the graph by peaking just above 0.950. The performance of Wine and Sonar are similar, with the BDMO method running at the bottom of the graphs of the two datasets starting from an average accuracy value of 0.65 and ending at around 0.66, although the curve peaked above 0.70 for the Sonar dataset. BGWO started from around 0.75 for both Wine and Sonar, and ended just below 0.75 and 0.78 respectively. BSNDO curves in both datasets run between 0.75, and BIEOSA similarly started just above 0.80 and ended just below 0.80 in both cases. As a characteristic of BPSO and BEOSA, both algorithms peaked the graphs for Wine and Sonar by starting from 0.88 and 0.94 on Wine, 0.83 and 0.82 on Sonar, then ending at 0.93 and 0.95 on Wine, and 0.88 and 0.86 on Sonar. The Tic-tac-toe dataset has BDMO at the bottom and BEOSA at the top, starting from 0.61 and 0.74 and ending at 0.60 and 0.79, respectively. BSNDO and BIEOSA ended their curves at around 0.64 but started at 0.63 and 0.65, respectively, while BWOA and BGWO started at the same point of 0.68 but ended at 0.66 and 0.69, respectively. The performance for BPSO showed that it peaked when the population size of 75 was used to obtain an accuracy value of 0.75. Experimental results for M-of-n, Ionosphere and Exactly are consistent for BPSO which tops the graphs of the three datasets by showing the lowest performance with population size 25 in all cases, but reported the best accuracies at 75, 50 and 75 population sizes each at 0.87, 0.925, and 0.84 accordingly for three datasets. BDMO lies at the lowest in M-of-n and Exactly but ranked second lowest in Ionosphere by obtaining its peaks at 0.61 for 75 population size, 0.810 for 75 population size, and 0.64 at 50 population size for M-of-n, Ionosphere and Exactly, respectively. The BSNDO reported straight curves in the three datasets. BIEOSA curves showed its peak classification accuracies at 0.74 using 100 population size, around 0.875 using 50 population size, 0.67 using 100 population size for M-of-n, Ionosphere and Exactly.