Fusion of Nonlinear Inertia Weight and Probability Mutation for Binary Particle Swarm Optimization Algorithm

With the rapid development of Internet information technology, vast amounts of textual data are constantly emerging. However, raw data is often unstructured and lacks readability, posing significant challenges for data mining. Feature selection, as a crucial step in machine learning, is essential for enhancing model performance. In this paper, we propose an Improved Binary Particle Swarm Optimization (IBPSO) algorithm that addresses the limited search capability of traditional swarm intelligence algorithms in high-dimensional optimization problems. By incorporating a dynamic nonlinear decreasing inertia weight update strategy and a local probability crossover mutation strategy, IBPSO effectively enhances algorithm performance while overcoming the limitations of conventional approaches. The proposed algorithm demonstrates superior performance through testing on benchmark functions.