Early and accurate diagnosis of Sjögren’s syndrome (SjD) remains a significant challenge due to the disease’s heterogeneous clinical presentation and the high dimensionality of transcriptomic data. Meta-heuristic optimizers are attractive for navigating such landscapes, yet existing algorithms tend either to over-explore or to converge prematurely. To address this, we propose DSMAL, a Differential-Evolution & Slime-Mould Algorithm with adaptive Refresh Local Search, as the first hybrid optimization framework tailored to SjD diagnostics. DSMAL partitions the population into two co-evolving sub-swarms: a Differential-Evolution (DE) cohort that drives broad exploration through differential mutation, and a Slime-Mould (SMA) cohort that intensifies exploitation via adaptive position updates. A novel Refresh Local Search (RLS) operator periodically re-diversifies both cohorts, mitigating stagnation without sacrificing convergence speed. For SjD diagnostics, DSMAL is integrated with an XGBoost classifier, forming the DSMAL-XGBoost model, which is trained and validated using gene expression profiles derived from three GEO datasets (GSE23117, GSE40611, and GSE84844). DSMAL optimizes XGBoost hyperparameters in a cross-validation loop to identify the most predictive feature set and classifier configuration. The final model is evaluated using an independent external test set (GSE7451) and demonstrates superior diagnostic performance, achieving 96.6% F1-score, 96.4% recall, 95.0% precision, and 97.6% AUC. Benchmark testing on the IEEE CEC 2021 suite further validates DSMAL’s theoretical strengths, outperforming ten state-of-the-art optimizers in both accuracy and computational efficiency. These findings underscore the potential of DSMAL-XGBoost as a robust tool for transcriptomic-based SjD diagnosis, with broader implications for complex autoimmune disease modeling.
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