Abstract: Traditional algorithm of blind source separation (BSS)is easy to fall into partial optimum value， and the convergence precision is low. In view of these disadvantages， the BSS method based on improved simplified particle swarm optimization algorithm was proposed， by which the whole particle swarm could be divided into several groups. Each particle was optimized while optimizing the whole area， and the difference between particles was increased. What’s more， premature convergence was avoided effectively. The negative entropy was taken as the objective function in the proposed algorithm， and the separation matrix was adjusted to separate each signal component from each other， so as to accomplish the blind source separation of instantaneous mixed signals. The simulation results show that the proposed algorithm is effective in avoiding premature convergence， and further improving convergence accuracy and algorithm stability compared with the basic particle swarm algorithm.

Key words: blind source separation(BSS), simplified particle swarm optimization(SPSO) algorithm, grouping, leapfrog algorithm, negentropy