Dynamic hierarchical cooperative Snow Ablation Optimizer for global and feature selection

To address the limitations of the Snow Ablation Optimizer (SAO) algorithm in solving complex multimodal and composite-structure problems, including slow convergence and susceptibility to local optima, this paper proposes the Hybrid Snow Ablation Optimizer (HGSAO) integrated with a dynamic multi-layer velocity guidance mechanism that integrates a dynamic hierarchical cooperative search mechanism and an adaptive local enhancement strategy. The core contribution of this work lies in the systematic deconstruction and reorganization of the original algorithmic framework through three progressive innovations: First, a dynamic hierarchical mechanism based on individual performance metrics and evolutionary trends is developed, which adaptively partitions the population into three functional subgroups (elite, potential, and ordinary), establishing a structural foundation for subsequent differentiated strategy implementation. Second, a hybrid update model for velocity and position evolution, incorporating historical information and population-state dynamics, is constructed, enabling targeted guided search operations for distinct subgroups to coordinate global exploration and local exploitation effectively. Third, an adaptive local enhancement strategy with dual perception capabilities for iteration progression and convergence status is embedded, which conducts multi-strategy collaborative exploitation in the neighborhood of high-quality solutions during the late algorithmic stages to improve convergence accuracy and stability. A comprehensive experimental evaluation on the IEEE CEC 2022 benchmark suite demonstrates that HGSAO achieves superior optimization accuracy, convergence speed, and robustness across unimodal, multimodal, hybrid, and composite functions, with the Wilcoxon rank-sum test confirming its statistically significant superiority. Furthermore, feature selection experiments conducted across 12 public datasets confirm HGSAO’s effectiveness in high-dimensional classification, achieving higher classification accuracy with reduced feature dimensions, thereby demonstrating strong potential for engineering applications.