动态神经引导提升蚁群优化，解决训练与推理不匹配问题
Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization
- 通过周期性观察信息素和当前解，实现动态引导
- 在10万节点的旅行商问题上优于神经基线，运行时间更短
- 适合大规模组合优化场景，尤其需长期迭代搜索的任务
神经引导蚁群优化（ACO）存在训练与推理不一致的根本问题：策略通常训练生成静态先验（如热力图），却用于指导迭代式、长时程的搜索过程。本文提出DyNACO框架，通过周期性观测信息素分布和当前最优解，实现动态神经引导。为保障大规模可扩展性，将策略与基于扰动的ACO后端及范围受限的精炼机制结合，兼顾效果与稳定信用分配。在旅行商问题（TSP）上，DyNACO可处理10万节点实例，性能超越神经基线，且总运行时间常低于无引导求解器。通过容量感知后端拓展至车辆路径问题（CVRP），持续提升无引导基线性能，神经开销低于1%。深入分析验证了模型泛化能力，并揭示动态引导优于静态先验的原因。研究强调学习引导优化中需对齐神经训练与迭代搜索动态。
原文摘要 · Abstract (English)
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.
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