TTPA: a two tiers PSO architecture for dimensionality reduction
Shikha Agarwal, Prabhat Ranjan · International Journal of Bio-Inspired Computation · 2019
Particle swarm optimisation (PSO) is a popular nature inspired computing method due to its fast and accurate performance, exploration and exploitation capability, cognitive and social behaviour and has fewer parameters to adjust. Recently, an improved binary PSO (IBPSO) was proposed by Chuang et al. (2008) to avoid getting trapped in local optimum and they have shown that it outperforms all other variants of PSO. Even though many variants of PSO exists independently to improve the performance of PSO, to escape from local optimum and to deal with dimensionality reduction, there still needs an integrated approach to handle it. Hence, in this paper, two tiers PSO architecture (TTPA) is proposed to find the maximum classification accuracy with minimum number of selected features. The proposed method is used to classify nine benchmarking gene expression datasets. The results show the merits of TTPA.