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 https://github.com/ai4co/gfacs.

该论文介绍了一种新型的神经引导元启发式算法，Generative Flow Ant Colony Sampler (GFACS)，用于组合优化。该算法将生成流网络 (GFlowNets) 与蚁群优化 (ACO) 方法相结合，在组合空间中提供了一个基于输入图实例的决策变量的先验分布，从而增强了 ACO 的功能。此外，本文还引入了一种新的训练技巧组合，包括搜索引导的局部探索、能量归一化和能量塑形，以提高 GFACS 的性能。我们的实验结果表明，在七个组合优化任务中，GFACS 胜过基准 ACO 算法，并且在车辆路径问题上与问题特定的启发式算法相竞争。源代码可在 https://github.com/ai4co/gfacs 上获得。