Feature selection is essential for improving classification performance in high-dimensional biomedical data, yet conventional metaheuristic algorithms often suffer from premature convergence and loss of population diversity. To address these issues, this paper proposes a Feature Grouping and Improved Hybrid Breeding Optimization framework (FGIHBO). First, the original feature space is hierarchically partitioned using the Maximum Relevance Minimum Redundancy criterion and Symmetric Uncertainty analysis to alleviate the curse of dimensionality. Then, a Multi-Strategy Synergistic Improved Hybrid Breeding Optimization (MSIHBO) algorithm is developed by incorporating Grey Wolf Optimizer (GWO) guidance and a Shannon entropy-adaptive simulated annealing mechanism to balance exploration and exploitation. Experimental results on the CEC2022 benchmark suite demonstrate that MSIHBO provides robust optimization performance across diverse problem categories. Furthermore, evaluations on eleven high-dimensional biomedical datasets show that FGIHBO achieves average classification accuracies ranging from 92.77% to 97.66%. Compared with representative algorithms, including Multi-strategy Improved Grey Wolf Optimizer (MIGWO), Hybrid Whale Optimization Algorithm based on Gathering strategy (HWOAG), Dynamic Crow Search Algorithm (DCSA), GWO, Hybrid Breeding Optimization (HBO), Hybrid Breeding Optimization based on Lévy flight and Elite Opposition-Based Learning strategy (LEHBO), and MSIHBO, the proposed framework improves average classification accuracy by 1.47–27.46%, with the largest gain observed on dataset D10 relative to HWOAG. These results confirm the effectiveness, robustness, and scalability of the proposed framework for high-dimensional biomedical feature selection.

Multi-label text feature selection aims to identify compact and discriminative feature subsets for documents associated with multiple correlated labels. In federated environments, this task is further complicated by high-dimensional search spaces, non-independent and non-identically distributed data, complex label dependencies, and the requirement that raw feature and label data remain local. To address these challenges, this paper proposes the Federated Anchor-Guided Frequency-Domain Optimization Algorithm (Fed-AGFDO), a federated multi-label text feature selection framework. At each client, the Anchor-Guided Frequency-Domain Optimization (AGFDO) module transforms the encoded search population into the frequency domain and combines random spectral exploration, elite-guided mixing, diversity-aware anchor screening, and multi-anchor direction aggregation to balance exploration and exploitation. Candidate feature-weight vectors are evaluated using a manifold-regularized fitness function that jointly considers sample structure, label-consensus regularization, sparsity regularization, and a soft global feature-weight consistency penalty. The server aggregates only local feature-weight vectors through sample-size-weighted aggregation followed by an exponential moving average update to smooth inter-round changes in the global feature weights while keeping raw data local. Experiments on eight multi-label text datasets demonstrate that Fed-AGFDO achieves strong classification and label-ranking performance with compact feature subsets. Under the representative subset settings, Fed-AGFDO improves Average Precision by up to $5.83\%$ over Fuzzy Federated Multi-Label Feature Selection (Fuzzy FMFS) and reduces Ranking Loss by up to $22.65\%$ relative to Federated Multi-Label Feature Selection (FMLFS). Parameter sensitivity and ablation analyses further demonstrate robustness across the examined parameter ranges and the complementary contributions of anchor guidance, diversity screening, and multi-anchor aggregation. These results indicate that Fed-AGFDO provides an effective and data-locality-aware solution for high-dimensional federated multi-label text feature selection.