We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances.

翻译：本文提出生成流蚁群采样器（GFACS），这是一种新颖的元启发式方法，通过分层方式结合了摊销推理与并行随机搜索。我们的方法首先利用生成流网络（GFlowNets）对组合解空间上的一个多模态先验分布进行摊销学习，该分布同时涵盖高奖励解与多样化解。随后，该方法以蚁群优化（ACO）的思想，通过并行随机搜索对此先验分布进行迭代更新，从而得到能够生成接近最优解的后验分布。在七个组合优化问题上的大量实验表明，GFACS 具有优异的性能表现。