Abstract
In this paper, the dynamic niching particle swarm optimization (DNPSO) is proposed to solve linear blind source separation problem. The key point is to use the DNPSO rather than particle swarm optimization (PSO) and fast-ICA as the optimization algorithm in Independent Component Analysis (ICA). By using DNPSO, which has global superiority, the performance of ICA will be improved in accuracy and convergence rate. The idea of sub-population in DNPSO leads to the greater efficiency compared with other methods when solving high dimensional cost functions in ICA. The performance of ICA based on DNPSO is investigated by numerical experiments.
Highlights
Blind source separation (BSS) is an advanced method in signal processing
We proposed Independent Component Analysis (ICA) based on Dynamic Niching Particle Swarm Optimization (DNPSO), which has greater global superiority in optimization progress of ICA algorithm
ICA based on DNPSO was tested with three speech signals shows in Figure.2, where totally 100000 sampling pixels are set as abscissa and amplitude are set as ordinates
Summary
Blind source separation (BSS) is an advanced method in signal processing. In recent years, BSS has gained increasing interest in various fields of applications. Since the traditional optimization algorithm, including gradient descent algorithm and natural gradient algorithm, are trapped into local optimum and have slow convergence rate, researchers proposed Particle Swarm Optimization (PSO) and the its improved algorithm as the optimization algorithm for ICA and have achieved remarkable experimental result [1]. We proposed ICA based on Dynamic Niching Particle Swarm Optimization (DNPSO), which has greater global superiority in optimization progress of ICA algorithm. The widely using of Niche Technique in Genetic Algorithm (GA) in recent years has interpreted its remarkable performance in global optimization of highdimensions functions [5].
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