Abstract
Aiming at the problem of recommendation systems, this paper proposes a fuzzy clustering algorithm based on particle swarm optimization. This algorithm can find the best solution, using the capacity of global search in PSO algorithm with a powerful global and defining a proportion factor, which can adjust the position and reduce the search space automatically. Then using mutation particles it replaces the particles flying out the solution space by new particles during the searching process. In order to check the performance of the proposed algorithm, by testing with typical ZDT1, ZDT2, ZDT3 functions, the experimental results show that the improved method not only has a better ability to converge to the global point, but can also efficiently avoid premature convergence.