# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
## 9 Conclusions and Future Directions  
In this paper, three hybrid approaches were proposed to provide a solution to the FS optimization problem based on S2 and V2 binary TFs that were used to convert continuous solutions to binaries. In this work, the three hybrid approaches were proposed to enhance FS optimization by combining BGWO with BPSO. The objective of this combination was to resolve the GWO stuck-in-local-optima problem that can occur by leveraging the PSO-wide search space exploration ability on the solutions obtained by GWO. A set of high-dimensional, small-instance medical datasets was utilized to evaluate the three proposed approaches. The datasets included multiclass, cancer-related human gene expression datasets. The evaluation experiments were performed to compare the three proposed approaches against the original BPSO and BPGWO, eight state-of-the-art FS optimizers, and, finally, one of the recent binary FS optimizers that combines BPSO with BGWO algorithms. The evaluation results showed the superiority of IBGWO4-V compared with other investigated approaches in terms of a set of evaluation metrics, which were ACA, ASE, AFV, and ACT. The computational complexity of all proposed approaches was investigated and it was shown that the most complex approaches were IBGWO3 and IBGWO4. In summary, the results showed that IBGWO4-V is the most effective approach and provides the best balance between global and local searching techniques for FS optimization tasks. The results also confirmed the importance of selecting a suitable TF to increase the performance of the optimizers. One of the salient limitations of the best-proposed approach, i.e., IBGWO4, is its computational complexity relative to the native BGWO, the native BGWO, and the other two proposed approaches, i.e., IBGWO2 and IBGWO3. When selecting one of the three proposed approaches, there should be a trade-off between the classification accuracy and fitness on one side and the computational complexity on the other side. Future work will include a set of extensions as follows. Firstly, 10-fold cross-validation data splitting will be applied instead of the 80% training/20% testing data splitting approach. Secondly, the proposed algorithms will be further tested using unimodal functions, multi-modal functions, and fixed dimensional benchmark functions [79]. This evaluation will provide a deeper analysis of the performance of the three proposed optimizers and show how these optimizers can overcome the issue of entrapment in local optima. Thirdly, a numerical analysis that shows the relation between the performance of the three proposed approaches and the degree of dimensionality of the different datasets needs to be implemented in a future extension of this work. Fourthly, the authors plan to incorporate more advanced metaheuristic methods like the work proposed in [51; 52; 58; 59] so that such work can be compared with the proposed approaches in a future extension of this work. A fifth extension of this work will include utilizing the PSO's searching mechanism to improve other SI methods, i.e., instead of GWO, which suffers from the problem of being stuck in local optimum solutions. Finally, the three proposed approaches can also be evaluated by using other common classifiers such as Support Vector Machines [80] and Artificial Neural Networks [81] to ensure that the proposed approaches maintain superiority and stability. Conceptualization, J.Y.K., A.K. and A.Y. ; methodology, J.Y.K. ; software, J.Y.K. ; validation, J.Y.K. and A.Y. ; formal analysis, J.Y.K. and A.Y. ; investigation, J.Y.K. ; resources, J.Y.K. ; data curation, J.Y.K. ; writing--original draft preparation, J.Y.K. ; writing--review and editing, J.Y.K. and A.Y. ; visualization, J.Y.K. and A.Y. ; supervision, A.K. and A.Y. ; project administration, A.K. and A.Y. ; funding acquisition, J.Y.K. All authors have read and agreed to the published version of the manuscript. The APC was funded by Palestine Technical University-Kadoorie. Not applicable.