Feature selection via a multi-swarm salp swarm algorithm

Feature selection (FS) is a promising pre-processing step before performing most data engineering tasks. The goal of it is to select the optimal feature subset with promising quality from the original high-dimension feature space. The Salp Swarm Algorithm (SSA) has been widely used as the optimizer for FS problems. However, with the increase of dimensionality of original feature sets, the FS problems propose significant challenges for SSA. To solve these issues that SSA is easy to fall into local optimum and have poor convergence performance, we propose a multi-swarm SSA (MSSA) to solve the FS problem. In MSSA, the salp swarm was divided into three sub-swarms, the followers updated their positions according to the optimal leader of the corresponding sub-swarm. The design of multi-swarm and multi-exemplar were beneficial to maintain the swarm diversity. Moreover, the updating models of leaders and followers were modified. The salps learn from their personal historical best positions, which significantly improves the exploration ability of the swarm. In addition, an adaptive perturbation strategy (APS) was proposed to improve the exploitation ability of MSSA. When the swarm stagnates, APS will perform the opposition-based learning with the lens imaging principle and the simulated binary crossover strategy to search for promising solutions. We evaluated the performance of MSSA by comparing it with 14 representative swarm intelligence algorithms on 10 well-known UCI datasets. The experimental results showed that the MSSA can obtain higher convergence accuracy with a smaller feature subset.