Feature selection based on particle swarm optimal with multiple evolutionary strategies

Feature selection is an effective data preprocessing step to reduce the dimension of feature space and save storage space. Binary particle swarm optimization (BPSO) has been applied successfully to solve feature selection problem. But it was easy to fall into local optimal point. M2BPSO was an improved BPSO algorithm. The particles of M2BPSO were updated by using various evolutionary strategies according to the performance of them, in addition, the mutation was used to overcome premature. In this paper, we adopted M2BPSO to solve feature selection problem. To test the validity of the algorithm, we compared it with various versions of BPSO methods. Experimental results showed that M2BPSO could effectively solve the feature selection problem.