Feature selection plays a crucial role in machine learning. For evolutionary computation based feature selection, the same particles may be repeatedly generated many times during the population iteration process, and recalculating the fitness values of these particles will cost a large amount of computational resources. Therefore, it is necessary to find these particles fast and skip calculating their fitness to save computation resources. This paper proposes the combination of MD5 encryption algorithm and the competitive swarm optimizer (CSO) algorithm for feature selection. Every particle in the population generated by CSO is encoded with MD5 encryption algorithm. Then the HashMap is used to quickly search for the repetitive particles. Through MD5 coding and HashMap searching, the repetitive particles are found fast, and then the recalculation of the repeated particles are avoid. Experimental results show that our proposed algorithm can significantly reduce the running time of feature selection for low-dimensional data, medium-dimensional data and high-dimensional data. Moreover, our algorithm does not change the framework of the competitive population optimization algorithm, only avoids calculation of the repeatedly generated particles, and therefore has no effect on the accuracy and number of features. Our algorithm can be widely applied to other computational intelligence methods.