relation: https://khub.utp.edu.my/scholars/12180/
title: Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection
creator: Al-Tashi, Q.
creator: Abdul Kadir, S.J.
creator: Rais, H.M.
creator: Mirjalili, S.
creator: Alhussian, H.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2019
type: Article
type: PeerReviewed
identifier:   Al-Tashi, Q. and Abdul Kadir, S.J. and Rais, H.M. and Mirjalili, S. and Alhussian, H.  (2019) Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection.  IEEE Access, 7.  pp. 39496-39508.  ISSN 21693536     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065103601&doi=10.1109%2fACCESS.2019.2906757&partnerID=40&md5=ec600f296b2a0e1986aecc74e526443c
relation: 10.1109/ACCESS.2019.2906757
identifier: 10.1109/ACCESS.2019.2906757