Recently, many evolutionary algorithms (EAs) have been used to solve clustering problem. However, compared to K-means which is a simple and fast clustering algorithm, these EA-based clustering algorithms take too much computation time. In addition, the parameters of most EAs are fixed or dynamical adjustment by a simple method on different datasets, and it will cause that the performance of these algorithms is good on some datasets but bad on others. In order to overcome these disadvantages, a novel parallel object-tracking behavior algorithm (POTBA) based on dynamics is proposed in this paper. The proposed algorithm consists of three different models which are parallel object-tracking model, parameters self-learning model and energy model, respectively. First, the parallel object-tracking model is designed to accelerate the computation speed and avoid local minima. Second, the parameters of POTBA are self-adjusted by the parameters self-learning model. Third, the energy model is introduced to depict energy changes of POTBA during the evolutionary process. The correctness and convergence properties of POTBA are analyzed theoretically. Moreover, the effectiveness and parallelism of POTBA are evaluated through several standard datasets, and the experimental results demonstrate that POTBA exhibits superior overall performance than five other state-of-the-art algorithms. In the aspect of search performance, the results of POTBA are better than other comparison algorithms on most used datasets. In the aspect of time performance, the time overhead of POTBA is significantly reduced through parallel computing. When the number of processors increases to 32, the computation time of POTBA is less or close to K-means which is the fastest comparison algorithm.