Improved Harris Hawks Algorithm and Its Application in Feature Selection

This research focuses on improving the Harris' Hawks Optimization algorithm (HHO) by tackling several of its shortcomings, including insufficient population diversity, an imbalance in exploration vs. exploitation, and a lack of thorough exploitation depth. To tackle these shortcomings, it proposes enhancements from three distinct perspectives: an initialization technique for populations grounded in opposition-based learning, a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration, and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators. The effectiveness of the Improved Harris Hawks Optimization algorithm (IHHO) is assessed by comparing it to five leading algorithms across 23 benchmark test functions. Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities. Additionally, this paper introduces a feature selection method leveraging the IHHO algorithm (IHHO-FS) to address challenges such as low efficiency in feature selection and high computational costs (time to find the optimal feature combination and model response time) associated with high-dimensional datasets. Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets. The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality, while also enhancing the efficiency of feature selection. Furthermore, IHHO-FS shows strong competitiveness relative to numerous algorithms.