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In a code-division multiple-access system, multiuser detection (MUD) can exploit the information of signals from other interfering users to increase system capacity. However, the optimum MUD can be characterized as an NP-hard combinatorial optimization problem such that the computational complexity increases exponentially with the number of users. In this paper, we apply a new evolutionary algorithm, called particle swarm optimization (PSO), to develop a suboptimal MUD strategy. The decorrelating detector (DD) or linear minimum mean square error (LMMSE) detector is used as the first stage to initialize the PSO-based MUD. Then, the PSO algorithm is applied to detect the received data bit by optimizing an objective function incorporating the linear system of the DD or LMMSE detector. Simulation results show that the performance of our proposed decorrelating PSO and LMMSE-PSO MUD are promising and outperform the decorrelating and LMMSE MUD with a slight increase in computation.