Blind source separation (BSS) is an advanced method of signal processing. Essentially, the problem in BSS is to separate and estimate the original signal from the observed mixed signal source without knowing the characteristics of the original signal. Independent component analysis (ICA) is a popular approach for blind source separation, and because its traditional search scheme is based on a gradient algorithm, a convergence problem will arise. In order to overcome the defect, this paper proposed to apply Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to conduct accelerated computing of the rate of convergence of a demixing matrix in ICA. However, the PSO converges prematurely, and the population diversity is reduced rapidly, so that the optimal solution falls into the local optimum. In order to increase the diversity of PSO, GPSO-based ICA algorithm (GPSO-ICA) is proposed that has the exploring ability of GSA, so that the ICA algorithm has a higher convergence rate and better ability to escape local optimization. A series of comparisons is implemented for the ICA algorithms based on PSO, GSA, and GPSO. The results show that GPSO-ICA has better performance than the other methods.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Artificial Intelligence