Boolean Particle Swarm Optimization with various Evolutionary Population Dynamics approaches for feature selection problems

In the feature selection process, reaching the best subset of features is considered a difficult task. To deal with the complexity associated with this problem, a sophisticated and robust optimization approach is needed. This paper proposes an efficient feature selection approach based on a Boolean variant of Particle Swarm Optimization (BPSO) boosted with Evolutionary Population Dynamics (EPD). The proposed improvement assists the BPSO to avoid local optima obstacles via boosting its exploration ability. In the BPSO-EPD, the worst half of the solutions are discarded by repositioning them around the optimal solutions selected from the best half. Six natural selection mechanisms comprising Best-based, Tournament, Roulette wheel, Stochastic universal sampling, Linear rank, and Random-based are employed to select guiding solutions. To assess the performance of the proposed improvement, 22 well-regarded datasets collected from the UCI repository are employed. The experimental results demonstrate the superiority of the proposed EPD-based feature selection approaches, especially the BPSO-TEPD variant when compared with conventional BPSO and other five EPD-based variants. Taking SpecEW dataset as an example, an increment of 6.7% accuracy can be achieved for BSPO-TEPD. Consequently, BPSO-TEPD approach also outperformed other well-known optimizers, including two binary variants of PSO using S-shaped transfer function (SBPSO) and V-shaped transfer function (VBPSO), Binary Grasshopper Optimization Algorithm (BGOA), Binary Gravitational Search Algorithm (BGSA), Binary Ant Lion Optimizer (BALO), Binary Bat algorithm (BBA), Binary Salp Swarm Algorithm (BSSA), Binary Whale Optimization Algorithm (BWOA), and Binary Teaching-Learning Based Optimization (BTLBO). The result emphasizes the excellent behavior of EPD strategies in evolving the ability of BPSO when dealing with feature selection problems.