In this study, a novel particle swarm optimization (PSO) integrated with Taguchi method will be introduced. We use Taguchi method to assist PSO in finding the optimum in each dimension of position vectors during iterations, and exploit those optima to derive a new best-adaptive position vector (particle) afterward. Through verification over six benchmark functions, we have compared this PSO-Taguchi algorithm with the traditional global and local versions of PSO, and have found that the PSO-Taguchi method has a superior performance in convergence rate. In this paper, PSO will be first introduced. Then Taguchi method and its characteristics will be reviewed. Next, the issue of slow convergence speed with regard to the traditional PSO will be discussed. Finally, in order to solve this issue, a novel PSO-Taguchi algorithm will be proposed and verified through simulations.