Hybrid Butterfly-Grey Wolf Optimization (HB-GWO): A Novel Metaheuristic Approach for Feature Selection in High-Dimensional Data

Abstract

Feature selection is a critical preprocessing step in high-dimensional data analysis, aiming to enhance model performance by eliminating irrelevant and redundant features. This paper introduces a novel hybrid metaheuristic algorithm, the Hybrid Butterfly-Grey Wolf Optimization (HB-GWO), which synergizes the global exploration capabilities of the Butterfly Optimization Algorithm (BOA) with the local exploitation strengths of the Grey Wolf Optimizer (GWO) to achieve an effective balance between exploration and exploitation in feature selection tasks. The algorithm incorporates an adaptive switching mechanism that dynamically adjusts the contribution of BOA and GWO throughout the optimization process. HB-GWO was evaluated on multiple benchmark datasets, including Breast Cancer, Madelon, Colon Cancer, and Arrhythmia, using a Random Forest classifier as the evaluation model. Experimental results demonstrate that HB-GWO consistently outperforms state-of-the-art metaheuristic algorithms (GA, PSO, BOA, GWO) in classification accuracy, feature reduction rate, and computational efficiency. An ablation study further confirms the contribution of each component of the hybrid algorithm. These findings position HB-GWO as a robust and efficient method for feature selection in high-dimensional data analysis.