Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection

By Qasem Al-Tashi, Said Jadid Abdul Kadir, Helmi Md Rais, Seyedali Mirjalili, Hitham Alhussian. IEEE Access, 2019

397 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8235898f50ddec1e91b2c613765d62e69564d8fd

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Citations in the corpus

A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “proposed a binary hybrid GWO and PSO algorithm for the feature selection problem [167].”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, methodology): “…worst fitness, and best fitness are used for feature selection in too many studies such as Genetic Algorithm (GA) [11], Grey Wolf Optimizer (GWO) [12], Particle Swarm Optimization (PSO) [13], hybrid PSO– GWO [14], Bowerbird Optimizer (SBO) [15], Biogeography-Based Optimizer (BBO) [16], Bat…”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “These application domains can be categorized into: machine learning (Emary et al., 2016, 2015; Al-Tashi et al., 2019), engineering (Kohli and Arora, 2018; Yıldız and Yıldız, 2018), wireless sensor network (Kaushik et al., 2019; Rajakumar et al., 2017), environmental modeling (Tikhamarine et al.,…”