  Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs).
Traditionally, customizing ACO for a specific problem requires the expert
design of knowledge-driven heuristics. In this paper, we propose DeepACO, a
generic framework that leverages deep reinforcement learning to automate
heuristic designs. DeepACO serves to strengthen the heuristic measures of
existing ACO algorithms and dispense with laborious manual design in future ACO
applications. As a neural-enhanced meta-heuristic, DeepACO consistently
outperforms its ACO counterparts on eight COPs using a single neural
architecture and a single set of hyperparameters. As a Neural Combinatorial
Optimization method, DeepACO performs better than or on par with
problem-specific methods on canonical routing problems. Our code is publicly
available at https://github.com/henry-yeh/DeepACO.
