Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization 事件

Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization arXiv:2606.04039v1 Announce Type: cross Abstract: 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