Feature selection is a crucial data processing method used to reduce dataset dimensionality while preserving key information. In this paper,
Feature selection is a crucial data processing method used to reduce dataset dimensionality while preserving key information. In this paper, we proposed a multi-strategy enhanced dung beetle optimization algorithm (mDBO) that integrates multiple strategies to effectively address the feature selection problem. First, a novel population initialization strategy based on a hybrid tent-sine map and random opposition-based learning was proposed to generate initial population. This strategy yielded a more uniform distribution of the initial population, significantly improving the quality of the population distribution within the search space. Second, a new differential evolution mutation strategy with a periodic retrospective adaptive mutation factor was proposed. This strategy effectively improved the algorithm's ability to jump out of the local optimal and explore potential candidate solutions. Third, based on Padé approximation technology and the novel adaptive evolutionary boundary constraint method, an innovative approximation strategy was proposed. The strategy was integrated into the framework of the dung beetle optimizer, significantly improving the solution accuracy and population quality of the algorithm. Finally, the binary version of the mDBO algorithm (bmDBO) was applied to feature selection tasks. Experiments entailing CEC2017 benchmark functions and 17 datasets showed that both mDBO and bmDBO outperformed other algorithms. The mDBO method outperformed other algorithms in 11 of the 29 benchmark functions, ranked second in 8 functions, and achieved an average rank of 1.62 in the Friedman ranking, securing the overall first place; the bmDBO method outperformed in 12 of 17 datasets, achieving an average ranking of 1.35 in the Friedman ranking, securing the first position.
