Aiming at the issue of underdetermined blind source separation （UBSS）， when using the density based spatial clustering of applications with noise （DBSCAN） algorithm to estimate the cluster center， it is easy to fall into the local optimum. Therefore， the accuracy of the mixing matrix composed of the cluster center coordinates is reduced， resulting in unsatisfactory signal separation results. This paper proposes a cuckoo adaptive search swarm optimization based on DBSCAN （CASSO-DBSCAN） algorithm. The algorithm enhances the global adaptive search ability based on the Levy flight strategy， and uses the idea of learning from the group to refine the optimization to obtain the optimal solution， which can estimate the cluster centers more accurately. The paper verifies the algorithm through the simulation of blind source separation of speech signals. Results show that it can effectively improve the estimation accuracy of the underdetermined mixing matrix and has good robustness， which proves the feasibility of the algorithm.