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.