Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection

Qasem Al-Tashi, Said Jadid Abdul Kadir, Helmi Md Rais, Seyedali Mirjalili, Hitham Alhussian

Research output: Contribution to journalArticle

26 Citations (Scopus)

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.

Original languageEnglish
Article number8672550
Pages (from-to)39496-39508
Number of pages13
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes

    Fingerprint

Keywords

  • classification
  • Feature selection
  • grey Wolf optimization
  • hybrid binary optimization
  • particle swarm optimization

Cite this

Al-Tashi, Q., Abdul Kadir, S. J., Rais, H. M., Mirjalili, S., & Alhussian, H. (2019). Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection. IEEE Access, 7, 39496-39508. [8672550]. https://doi.org/10.1109/ACCESS.2019.2906757