MATLAB code for BGWOPSO: Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection Paper Reference - Al-Tashi, Q., Kadir, S. J. A., Rais, H. M., Mirjalili, S., & Alhussian, H. (2019). Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access, 7, 39496-39508. Link for algorithm details: Paper https://ieeexplore.ieee.org/abstract/document/8672550 Running the code Set all the required parameters run file demo.m
Abstract: A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.
Cite As Al-Tashi, Qasem, et al. “Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection.” IEEE Access, vol. 7, Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 39496–508, doi:10.1109/access.2019.2906757.
Qasem Al-Tashi (2021). Binary Optimization Using Hybrid GWO for Feature Selection (https://github.com/qasemabdullah/Hybrid-Binary-GWO-FS), GitHub. Retrieved .
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