Particle swarm optimization (PSO) algorithm, originated as a simulation of a simplified social system, is an evolutionary computation technique developed successfully in recent years. In this paper the binary and real-valued versions of PSO algorithm are exploited in two important signal processing paradigm: multiuser detection (MUD) and blind extraction of sources (BES), respectively. The novel approaches are effective and efficient with parallel processing structure and relatively feasible implementation. Simulation results validate either PSO-MUD or PSO-BES has a significant performance improvement over conventional methods.