TY  - JOUR
VL  - 7
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065103601&doi=10.1109%2fACCESS.2019.2906757&partnerID=40&md5=ec600f296b2a0e1986aecc74e526443c
A1  - Al-Tashi, Q.
A1  - Abdul Kadir, S.J.
A1  - Rais, H.M.
A1  - Mirjalili, S.
A1  - Alhussian, H.
JF  - IEEE Access
Y1  - 2019///
KW  - Benchmarking; Classification (of information); Feature extraction; Genetic algorithms; Nearest neighbor search; Problem solving; Simulated annealing
KW  -  Benchmark datasets; Binary genetic algorithm; Binary optimization; Feature selection problem; Hybrid optimization algorithm; K-nearest neighbors classifiers; Optimization algorithms; Performance measure
KW  -  Particle swarm optimization (PSO)
ID  - scholars12180
N2  - 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.
EP  - 39508
SN  - 21693536
PB  - Institute of Electrical and Electronics Engineers Inc.
SP  - 39496
TI  - Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection
N1  - cited By 298
AV  - none
ER  -