Abstract: The performance of existing blind source separation methods is affected by the non-linear contrast function that is selected according to the distribution of original signals.To solve this problem,a blind source separation algorithm based on adaptive particle swarm optimization is proposed,which takes the negentropy of mixtures as a contrast function.The inertia weight factor depends on the negentropy,which can improve the contradiction between the convergence speed and the performance of separated signals.The simulated results show that the proposed method could separate the mixture of both super-Gaussian signal and sub-Gaussian signal,and the proposed algorithm could efficiently alleviate the problem of premature convergence and has a faster convergence speed than PSO.