A Multi-Strategy Harris Hawks Optimization and Its Application in Feature Selection

Feature selection (FS) is a pivotal preprocessing task in data mining aimed at identifying optimal feature subsets to improve model generalization and reduce computational overhead. However, its NP-hard nature poses significant challenges for traditional optimizers in terms of search efficiency and solution quality. The Harris Hawks Optimization (HHO) algorithm is a state-of-the-art population-based metaheuristic method that demonstrates powerful capabilities in various optimization challenges. Despite its advantages, HHO encounters problems such as early stagnation and reduced accuracy. To mitigate these problems, we introduce an advanced algorithm called the Hybrid Strategy Harris Hawks Optimization (HSHHO). The HSHHO combines three key enhancements to support global search diversity and local refinement: (1) an exploration mechanism that utilizes the Self-Parameterized Map (SPM) alongside a dynamic logarithmic spiral to expand search breadth; (2) a nonlinear adjustment to the escape energy parameter for improved phase equilibrium; and (3) an elite perturbation approach that uses Cauchy–Gaussian mutation to strengthen local optimization and solution quality. We assessed HSHHO against eight well-known algorithms on 30 benchmark functions, where it exhibited superior results in the majority of cases. Finally, HSHHO is applied to address 18 feature selection tasks. The results demonstrated that HSHHO achieved highly competitive outcomes in terms of objective values, feature subset size, and classification performance in most datasets, reaching an average accuracy of 94.47%.