Improvements of bat algorithm for optimal feature selection: A systematic literature review

Abstract

Bat Algorithm (BA) has been extensively applied as an optimal Feature Selection (FS) technique for solving a wide variety of optimization problems due to its impressive characteristics compared to other swarm intelligence methods. Nevertheless, BA still suffers from several problems such as poor exploration search, falling into local optima, and has many parameters that need to be controlled appropriately. Consequently, many researchers have proposed different techniques to handle such problems. However, there is a lack of systematic review on BA which could shed light on its variants. In the literature, several review papers have been reported, however, such studies were neither systematic nor comprehensive enough. Most studies did not report specifically which components of BA was modified. The range of improvements made to the BA varies, which often difficult for any enhancement to be accomplished if not properly addressed. Given such limitations, this study aims to review and analyse the recent variants of latest improvements in BA for optimal feature selection. The study has employed a standard systematic literature review method on four scientific databases namely, IEEE Xplore, ACM, Springer, and Science Direct. As a result, 147 research publications over the last ten years have been collected, investigated, and summarized. Several critical and significant findings based on the literature reviewed were reported in this paper which can be used as a guideline for the scientists in the future to do further research.