A Transformer-Initialized Dual-Population Evolution for Large-Scale Task Scheduling in Heterogeneous Distributed Systems

Task scheduling in heterogeneous distributed systems is critical for industrial platforms, where decisions must be made under strict time constraints while resource states evolve dynamically. Existing approaches face significant limitations: classical heuristics yield suboptimal solutions; metaheuristics scale poorly; learning-based methods require extensive training with limited generalization. This article proposes a transformer-initialized dual-population evolution (TIDE), integrating three innovations: first, enhanced graph coloring preprocessing for enriched task representation, second, Transformer-based cross-modal attention for intelligent initialization of feasible solutions without offline pretraining, supported by an online adaptation mechanism, and third, asymmetric dual-population cooperative optimization with adaptive dimensionality reduction. Comprehensive experiments demonstrate that TIDE consistently outperforms state-of-the-art metaheuristics by 8%–13% in makespan while achieving an 80%–85% reduction in algorithm computing time compared to the metaheuristic average. On real scientific workflows, TIDE improves resource utilization by 4%–6% and maintains load balance above 94%, while maintaining response times within industrial deadlines. These results establish TIDE as a scalable solution for real-time scheduling in large-scale industrial systems.