Feature selection (FS) is essential in machine learning (ML) and data mapping by its ability to preprocess high-dimensional data. By selecting a subset of relevant features, feature selection cuts down on the dimension of the data. It excludes irrelevant or surplus features, thus boosting the performance and efficiency of the model. Particle Swarm Optimization (PSO) boasts a streamlined algorithmic framework and exhibits rapid convergence traits. Compared with other algorithms, it incurs reduced computational expenses when tackling high-dimensional datasets. However, PSO faces challenges like inadequate convergence precision. Therefore, regarding FS problems, this paper presents a binary version enhanced PSO based on the Support Vector Machines (SVM) classifier. First, the Sand Cat Swarm Optimization (SCSO) is added to enhance the global search capability of PSO and improve the accuracy of the solution. Secondly, the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity. The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed. To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization (SCPSO), the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems. Compared with the standard PSO algorithm, SCPSO converges faster, and the convergence accuracy is significantly improved. Moreover, SCPSO's comprehensive performance far exceeds that of other algorithms. Six datasets from the University of California, Irvine (UCI) database were selected to evaluate SCPSO's effectiveness in solving feature selection problems. The results indicate that SCPSO has significant potential for addressing these problems.