Recently, particle swarm optimization (PSO) has become one of the most popular approaches to clustering problems because it can provide a higher quality result than deterministic local search method. The problem of PSO in solving clustering problems, however, is that it is much slower than deterministic local search method. This paper presents a novel method to speed up its performance for the partitional clustering problem-based on the idea of eliminating computations that are essentially redundant during its convergence process. In addition, the multistart strategy is used to improve the quality of the end result. To evaluate the performance of the proposed method, we compare it with several state-of-the-art methods in solving the data and image clustering problems. Our simulation results indicate that the proposed method can reduce from about 60% up to 90% of the computation time of the &-means and PSO-based algorithms to find similar or even better results.