In order to overcome the disadvantages of slow convergence speed and poor separation accuracy when solving blind source separation (BSS) problems in traditional swarm intelligence algorithms, a BSS method based on improved elephant herding optimization (IEHO) is proposed. The method uses the principle of independence to construct the objective function, combining the kurtosis and the mutual information of separating signals. In the clan update stage, the scale factor of algorithm is modified as well as exploiting the neighborhood search to improve the diversity of the search. In the separation stage, the quantum-behaved particle swarm optimization strategy is introduced to improve the global search ability of the algorithm. The simulation results show that the IEHO algorithm has a better optimization effect compared with the traditional elephant herding optimization algorithm and the particle swarm optimization algorithm. Meanwhile, the proposed algorithm can also realize blind source separation of images and speech successfully, with higher separation accuracy and faster convergence speed.