Dynamic Firefly Algorithm for Feature Selection Based on Neighborhood Granularity Conditional Entropy

To address the slow convergence and susceptibility of the traditional Firefly Algorithm (FA) to local optima in solving optimization problems, this paper proposes a dynamic firefly algorithm. The proposed algorithm is integrated with neighborhood rough set theory for feature selection, effectively processing continuous values and enhancing the performance of feature selection. The algorithm improves the FA search strategy by incorporating the Precedence Operation Crossover (POX) mutation strategy and threshold settings to control the probability of firefly crossover and mutation, thereby enabling individuals trapped in local optima to escape. Furthermore, it introduces a new information entropy model-the neighborhood granular conditional entropy-by combining neighborhood knowledge granularity with conditional entropy to balance knowledge completeness and granularity. The feature selection algorithm FS_NGHFAPOX, which is based on neighborhood granular conditional entropy and the dynamic firefly algorithm, constructs the fitness function to improve the evaluation of feature subsets. Experiments conducted on several datasets from the UCI repository and built-in databases of the scikit-learn machine learning library demonstrate that the FS_NGHFAPOX algorithm achieves optimal classification performance with a smaller number of selected feature subsets. Specifically, the FS_NGHFAPOX algorithm achieved an average accuracy of 0.83 on the experimental datasets, which is up to 15% higher than those of the other feature selection algorithms.