Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection.

Feature selection (FS) is a significant dimensionality reduction technique, which can effectively remove redundant features. Metaheuristic algorithms have been widely employed in FS, and have obtained satisfactory performance, among them, grey wolf optimizer (GWO) has received widespread attention. However, the GWO and its variants suffer from limited adaptability, poor diversity, and low accuracy when faced with high-dimensional data. The hybrid rice optimization (HRO) algorithm is an emerging metaheuristic algorithm derived from the hybrid heterosis and breeding mechanism in nature. It possesses a robust capacity to identify and converge towards optimal solutions. Therefore, a novel approach based on multi-strategy collaborative GWO combined with the HRO algorithm (HRO-GWO) for FS is proposed in this paper. The HRO-GWO algorithm is enhanced by four innovative strategies including dynamical regulation strategy and three search strategies. First, to improve the adaptability of GWO, the dynamical regulation strategy is devised for parameter optimization of GWO. Then, a multi-strategy co-evolution model inspired by HRO is designed, which utilizes neighborhood search, dual-crossover, and selfing techniques to bolster population diversity. Finally, the study develops a hybrid filter-wrapper framework incorporating chi-square and the HRO-GWO algorithm to efficiently select pertinent and informative feature subsets, enhancing the classification performance while conserving time. The performance of HRO-GWO has been rigorously assessed across benchmark functions and the effectiveness of the proposed framework has been evaluated on small-sample high-dimensional biomedical datasets. Our experimental findings demonstrate that the approach on the basis of HRO-GWO outperforms state-of-the-art methods.