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A Diversity-Guided Particle Swarm Optimization Method for Blind Source Separation

This paper presents a diversity-guided Particle swarm optimization (PSO) algorithm to resolve the Blind source separation (BSS) problem. Because the independent component analysis (ICA) approach, a popular method for the BSS problem, has a shortcoming of premature convergence during the optimization process, the proposed PSO algorithm aims to improve this issue by using the diversity calculation to avoid trapping in the local optima. In the experiment, the performance of the proposed PSO algorithm for the BSS problem has been investigated and the results are compared with the conventional PSO algorithm. It shows that the proposed PSO algorithm outperforms the conventional PSO algorithm.