Explainable feature selection using improved firefly algorithm with population diversification and stagnation-aware exploration

Feature selection is an important preprocessing step in classification. By reducing dimensionality and removing irrelevant features, it can help improve classification performance. Swarm intelligence–based metaheuristic algorithms are widely used for feature selection. They are effective for exploring large and nonlinear search spaces. However, they often function as black-box approaches, providing limited transparency regarding the selection of particular features. The growing emphasis on eXplainable Artificial Intelligence has increased the need for interpretable decisions in sensitive domains such as healthcare and finance. This study focuses on improving the Firefly Algorithm for feature selection. The goal is to make the selection process easier to explain. It integrates SHapley Additive exPlanations values into the fitness function. The basic Firefly Algorithm is prone to premature convergence and local optima entrapment as it follows a single-leader attraction model. The proposed variant overcomes these limitations while maintaining a balance between exploration and exploitation. Experiments conducted on ten benchmark datasets show that the proposed approach achieves higher classification accuracy, more stable convergence, and feature subset sizes comparable to those of existing methods. The selected features also show strong overlap with SHAP-based importance rankings, enhancing the transparency of the feature selection process.