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Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has beensuccessfully applied to various Combinatorial Optimization Problems (COPs).Traditionally, customizing ACO for a specific problem requires the expertdesign of knowledge-driven heuristics. In this paper, we propose DeepACO, ageneric framework that leverages deep reinforcement learning to automateheuristic designs. DeepACO serves to strengthen the heuristic measures ofexisting ACO algorithms and dispense with laborious manual design in future ACOapplications. As a neural-enhanced meta-heuristic, DeepACO consistentlyoutperforms its ACO counterparts on eight COPs using a single neuralarchitecture and a single set of hyperparameters. As a Neural CombinatorialOptimization method, DeepACO performs better than or on par withproblem-specific methods on canonical routing problems. Our code is publiclyavailable at https://github.com/henry-yeh/DeepACO.

DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization