This paper introduces the Generative Flow Ant Colony Sampler (GFACS), a novel neural-guided meta-heuristic algorithm for combinatorial optimization. GFACS integrates generative flow networks (GFlowNets) with the ant colony optimization (ACO) methodology. GFlowNets, a generative model that learns a constructive policy in combinatorial spaces, enhance ACO by providing an informed prior distribution of decision variables conditioned on input graph instances. Furthermore, we introduce a novel combination of training tricks, including search-guided local exploration, energy normalization, and energy shaping to improve GFACS. Our experimental results demonstrate that GFACS outperforms baseline ACO algorithms in seven CO tasks and is competitive with problem-specific heuristics for vehicle routing problems. The source code is available at \url{https://github.com/ai4co/gfacs}.

翻译：本文提出了一种面向组合优化的新型神经引导元启发式算法——生成流蚁群采样器（GFACS）。GFACS将生成流网络（GFlowNets）与蚁群优化（ACO）方法相结合。GFlowNets作为一种在组合空间中学习构造策略的生成模型，通过提供基于输入图实例的先验分布来增强ACO的决策变量估计。此外，我们引入了一套创新的训练技巧组合，包括搜索引导的局部探索、能量归一化与能量整形，以改进GFACS性能。实验结果表明，GFACS在七个组合优化任务中优于基线ACO算法，并在车辆路径规划问题上与专用启发式方法具有竞争力。源代码开源地址为：\url{https://github.com/ai4co/gfacs}。