[Paper] Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization
Source: arXiv - 2606.04039v1
Overview
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 often reducing total runtime compared to the unguided solver. We extend DyNACO to CVRP via a capacity-aware backend, consistently improving the unguided baseline with less than 1% neural overhead. We further provide in-depth analysis validating the model’s generalization capabilities and elucidating why dynamic guidance outperforms static priors. Our work underscores the necessity of aligning neural training with iterative search dynamics in learning-guided optimization. The code is available at https://github.com/shoraaa/DyNACO.
Key Contributions
This paper presents research in the following areas:
- cs.NE
- cs.AI
- cs.LG
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.NE.
Authors
- Dat Thanh Tran
- Van Khu Vu
- Yining Ma
Paper Information
- arXiv ID: 2606.04039v1
- Categories: cs.NE, cs.AI, cs.LG
- Published: June 2, 2026
- PDF: Download PDF