%0 Journal Article
%@ 21693536
%A Al-Tashi, Q.
%A Abdul Kadir, S.J.
%A Rais, H.M.
%A Mirjalili, S.
%A Alhussian, H.
%D 2019
%F scholars:12180
%I Institute of Electrical and Electronics Engineers Inc.
%J IEEE Access
%K Benchmarking; Classification (of information); Feature extraction; Genetic algorithms; Nearest neighbor search; Problem solving; Simulated annealing, Benchmark datasets; Binary genetic algorithm; Binary optimization; Feature selection problem; Hybrid optimization algorithm; K-nearest neighbors classifiers; Optimization algorithms; Performance measure, Particle swarm optimization (PSO)
%P 39496-39508
%R 10.1109/ACCESS.2019.2906757
%T Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection
%U https://khub.utp.edu.my/scholars/12180/
%V 7
%X 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. © 2013 IEEE.
%Z cited By 298