## 5 Results and Discussion of Findings
### Comparative Analysis of Fitness and Cost Functions  
BEOSA and BIEOSA are now compared with related algorithms based on the results obtained for fitness and cost functions. The fitness function aims to minimize the objective function, while the cost function aims to maximize it. Table 3 lists the results obtained for each of the binarized algorithms for all benchmark datasets. The BWOA algorithms performed better on the BreastEW, Lung, Iris, Exactly2, Colon and Vote datasets, with fitness values of 0.0307, 0.0006, 0.0050, 0.2384, 0.0004 and 0.0013, respectively. BWOA showed superiority with six benchmark datasets, while BGWO showed superiority with WaveformEW, yielding a fitness value of 0.1817. BSNDO outperformed the other methods on eight datasets, i.e., Lymphography, M-of-n, PenglungEW, Sonar, SpectEW, Tic-tac-toe, Wine and KrVsKpEW, with fitness values of 0.0380, 0.0046, 0.0013, 0.0047, 0.0948, 0.1647, 0.0298 and 0.0250, respectively. Interestingly, BEOSA outperformed most of the other methods, showing superiority with nine datasets, i.e., CongressEW, Exactly2, HeartEW, Ionosphere, Prostate, Wine and Zoo, with fitness values of 0.0575, 0.2620, 0.2384, 0.0772, 0.0722, 0.0002, 0.0298 and 0.0533, respectively. Meanwhile, the associated variant of the proposed algorithm, BIEOSA, was competitive with BEOSA, showing superiority on two datasets. The implication of these findings is that the new method is suitable for minimizing the fitness function, allowing it to solve the difficult problem of feature selection on a wide range of datasets with different dimensionalities. The values obtained for the cost function are plotted in Figure 6 to show the variation in the performance of the algorithms with the various datasets. A close examination of the plots for the Zoo, Vote, Wine, Sonar and Tic-tac-toe datasets shows that BEOSA yielded outstanding cost values during the iterative process. In the five considered datasets, the BGWO method showed unstable performance on the cost function, whereas the BEOSA, BIEOSA, BDMO, BSNDO, BPSO, and BWOA were stable and BEOSA, BIEOSA, and BPSO often yielded similar results. The BEOSA curve was above those of the other methods for the Zoo, Sonar and Tic-tac-toe datasets and was close behind those of other methods for the Vote and Wine datasets. In the second category, we compared the performance curves of all the methods using M-of-N, Ionosphere, Exactly2, and HeartEW datasets. The BGWO maintained its unstable performance along the curve line, whereas all the remaining methods yielded good results. For example, BEOSA and BPSO closely shared the top section of the plots, meaning that their performance on the cost function was superior to those of the other methods. At the same time, both BDMO and BWOA were low in all the plots, showing that their performance in evaluating the cost function was poor. The BIEOSA and BSNDO were average performers in the five datasets. The third categories of datasets for comparison were Congress, Lymphography, BreastEW, Colon, and SpectEW. With the high dimensional Colon dataset, the BEOSA yielded similar results to BPSO and BGWO, even though the latter was unstable, while the variant BIEOSA and BSNDO methods demonstrated average performance. For the BreastEW dataset, both BEOSA and BIEOSA outperformed the other methods, yielding the best cost function curve. The BEOSA algorithm was just below that of BPSO on the Lymphography dataset, which superseded all other algorithms. The BIEOSA, BPSO, and BWOA were all plotted at the top section for the CongressEW datasets, while the BEOSA algorithm trailed behind. Similarly, the BEOSA outperformed all methods on the SpectEW datasets, although the BIEOSA algorithm yielded a curve in the lower section. Figure 6: _Cont_.