A novel hybrid feature selection method combining binary grey wolf optimization and cuckoo search.

The increasing availability of high-dimensional data in modern applications poses serious challenges for machine learning models, including excessive computation, high memory demand, and degraded accuracy caused by redundant or irrelevant features. This paper introduces a novel feature selection algorithm, Binary Grey Wolf Optimization with Cuckoo Search (BGWOCS), which distinguishes itself from previous hybrid GWO-based methods through its unique integration of nonlinear adaptive convergence for dynamic exploration-exploitation balance and Lévy flight-based alternation for enhanced global search. The proposed method combines the local exploitation capability of Binary Grey Wolf Optimization with the global exploration of Cuckoo Search, further incorporating a probabilistic variation mechanism to maintain population diversity and prevent premature stagnation. Experimental validation on ten benchmark UCI datasets demonstrates that BGWOCS achieves up to 4% higher classification accuracy and 15% fewer selected features compared to four competitive algorithms (HRO-GWO, GWOGA, MTBGWO, and IBGWO), with statistically significant improvements (p < 0.05).