Abstract

Metaheuristic optimization algorithms are widely used in wrapper-based feature selection, since they can search complex combinatorial spaces without gradient information. The standard sine cosine algorithm (SCA) is simple and easy to implement, but depends on the current population state and a single global optimal guide. This dependence can cause premature convergence and late-stage oscillations in discretized feature-selection tasks. To address these issues, we propose the fractional-order elite-memory sine cosine algorithm (FOSCA). The FOSCA integrates short-memory fractional-order position reconstruction, dynamic elite-center guidance and nonlinear search-factor decay into the SCA search dynamics. These mechanisms improve the trajectory continuity, guidance diversity and convergence stability. Experiments on 14 classification datasets showed that the FOSCA achieved a competitive accuracy, F1-score, precision and recall. The FOSCA also outperformed the original SCA on 12 out of the 14 datasets. Statistical tests, a stability analysis, a performance–sparsity trade-off analysis and ablation studies confirmed the effectiveness of the proposed mechanisms. Overall, the FOSCA improves the SCA search reliability in discrete feature selection and offers a reproducible basis for related optimizer-based methods.

IPC Classification

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