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 ba

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ethics.ai (2 June 2026), “Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization,” evidence record 3242, https://ethics.ai/record/3242 (originally published by arXiv).

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