Abstract: To reduce computational complexity of the optimal multi-user detector, a novel hybrid algorithm that employs particle swarm optimization algorithm (PSO) and Hopfield neural network is presented. Then we design a novel multi-user detector in code-division multiple-access (CDMA) communication systems. Using this approach, the Hopfield neural network is embedded into the PSO to improve further the performance of the population at each generation. Such a hybridization of the PSO with the Hopfield neural network reduces its computational complexity by providing faster convergence. Simulation results are provided to show that the proposed detector has significant performance improvements over the detectors based on Hopfield neural network, genetic algorithm, and particle swarm optimization in terms of multiple access interference and near-far resistance.