Application of Adaptive Parameter Chaotic Particle Swarm Optimization Algorithm for Blind Source Separation

Independent component analysis (ICA) is a blind source separation technology, and in its application process the objective function needs to be optimized, the traditional algorithm of particle (PSO) easily falls into local optimization, instability and other defects. In order to solve this problem, ICA is optimized by the parameter adaptive chaos particle swarm optimization algorithm. Firstly, PSO parameter are adaptive adjusted to improve the search ability of particle, and the particle is chaos disturbed to improve the convergence rate. The results show that the proposed method has solved the ICA objective function optimization problems, and greatly improve the blind source separation effect.