Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization
Neural-guided Ant Colony Optimization (ACO) suffers from a fundamental training-inference misalignment: policies are typically trained to generate static priors (e.g., heatmaps), yet deployed to guide iterative, long-horizon search processes. In this paper, we present DyNACO, a novel framework that achieves dynamic neural guidance by periodically observing the pheromone distribution and the incumbent solution. To make DyNACO tractable at scale, we pair the policy with a perturbation-based ACO backend and a scope-restricted refinement mechanism that jointly ensure efficacy and stable credit assignment. On TSP, DyNACO scales to 100,000-node instances and outperforms neural baselines while o...
| Authors | Dat Thanh Tran, Van Khu Vu, Yining Ma |
|---|---|
| Primary category | cs.NE |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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