## 6 Conclusions  
This study presents the design of binary variants of the EOSA and IEOSA algorithms, referred to as the BEOSA and BIEOSA optimizers. Using models to represent the binary search space and an optimization process to change from a continuous to a discrete search space, the study shows that the new methods are suitable. Furthermore, we investigated the performance impact of using different transfer functions in the exploitation and exploration of two S-functions and two V-functions. Exhaustive experimentation was carried out using over 20 datasets with a wide range of heterogeneous features, and a comparative analysis was made with the BDMO, BSNDO, BPSO, BWOA, BSFO and BGWO methods. The performance outcomes showed that both BEOSA and BIEOSA performed reasonably well with most of the datasets and demonstrated competitive results with the others. This evaluation was shown using the values obtained for the fitness and cost function and the number of selected features. Furthermore, the study examined the impact of the choice of classifier used for feature classification purposes with respect to the optimizer. The findings showed that KNN and SVM performed the feature classification tasks exceptionally well. Meanwhile, a comparative analysis of the runtime and a statistical analysis of the methods were also reported. The results showed that significant performance improvements could  
Figure 10: Comparison of the computational times for BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA on all the benchmark datasets.  
be achieved when the transfer functions were skillfully formulated and applied. This finding was supported by the fact that the separation of applicability of the S-function from the V-function in the exploration and exploitation phases enhanced the performance of the algorithm. This study advances research in this domain through a novel demonstration, i.e., using different transfer functions in the search process involving the exploration and intensification phase. Moreover, the formulation of new transfer functions adds to the novelty of the proposed binary methods. One limitation with the study is associated with the performance of the immunity-based method, IEOSA, whose binary variant was unable to compete with other methods, in contrast with BEOSA, which yielded similar results to other state-of-the-art classifiers. This limitation will require further fine-tuning to enhance the algorithm. In future, we propose investigating the use of competing optimization algorithms as a hybrid solution with the BEOSA and BIEOSA methods. This is motivated by the need to capitalize upon the advantages of other methods in order to reduce the limitations of the base EOSA method. Future research opportunities with respect to the proposed method may be centred on using deep learning-based feature extraction and classification procedures. This could possibly result in an outstanding hybrid model, which, to date, no study has considered. Another future work is to investigate the possibility of swapping the usage of the S-function and V-function and to compare the performance with that described in this study.  
Contributed to the conception and design of the research work, Material preparation, experiments, and analysis, O.A., O.N.O. and A.E.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
All codes used are available online at their indicated references.