An improved binary particle swarm optimization combing V-shaped and U-shaped transfer function

Feature selection aims to find a best feature subset from all feature sets of a given dataset, which represents the whole feature space to reduce redundancy and improve classification accuracy. The evolutionary computation algorithm is often applied to feature selection, but there exists low efficiency in the search process. With the increase of the number of features, solving the feature selection problem become more and more difficult. Existing evolutionary algorithms have many defects, such as slow convergence speed, low convergence accuracy and easy to fall into local optimum. Therefore, the research of more effective evolutionary algorithms has important theoretical significance and application value. Binary Particle Swarm Optimization (BPSO) is a kind of evolutionary computation algorithm and has a good performance in feature selection problems. It uses transfer function to convert the continuous search space to the binary one. Transfer function plays an important role in BPSO. So this paper proposes an improved BPSO by combining V-shaped and U-shaped transfer function, and introduces a new learning strategy and a local search strategy based on adaptive mutation. The improved BPSO enhances its optimization ability in feature selection problem. The experimental results show that the improved BPSO has better dimension reduction ability and classification performance than other algorithms.