Multiuser detector based on adaptive artificial fish school algorithm

Artificial school algorithm (AFSA) is a new kind of intelligence optimization algorithm, which has some advantages that genetic algorithm (GA) and particle swarm optimization (PSO) do not have. But this algorithm has several disadvantages such as the blindness of searching at the later stage and the poor ability to keep the balance of exploration and exploitation, which reduce its probability of searching the best result. To overcome these problems, two improved AFSA named AAFSA/spl I.bar/FS and AAFSA/spl I.bar/CS are proposed. The improved algorithms can adjust the searching range adaptively and have better ability to keep the balance of exploration and exploitation. Then we apply the new algorithms to solve the multiuser detection problems. Simulation results show that the proposed detectors outperform GA detector and PSO detector in terms of BER, near-far resistant and convergence performance.