Comparative Study of Different Salp Swarm Algorithm Improvements for Feature Selection Applications

Abstract

Recently, many optimization algorithms have been applied for Feature Selection (FS) problems and still showing very promising results. Moreover, Salp Swarm Algorithm (SSA) is one of the most sophisticated meta-heuristic swarm-based optimization algorithms. As SSA is proving its efficiency, it has undergone several improvements in order to make its performance as better as it could be. In this context, this paper presents a comparative study of the Salp Swarm Algorithm variants for feature selection application. These different improvement approaches aim to reduce the number of features and eliminate the non-useful ones. This study focuses on three binary versions of SSA, namely Binary Salp Swarm Algorithm (BSSA), Chaotic Salp Swarm Algorithm (CSSA) and Dynamic Salp Swarm Algorithm (DSSA). For this purpose, 13 UCI benchmark datasets were used. Based on the comparative results we can conclude that the DSSA approach enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature.