DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
This work addresses the need for reducing manual effort in customizing ACO for combinatorial optimization problems, offering a generic solution with broad applicability.
The paper tackles the problem of automating heuristic design for Ant Colony Optimization (ACO) in combinatorial optimization, proposing DeepACO, a neural-enhanced framework that outperforms ACO counterparts on eight problems and matches problem-specific methods on routing tasks.
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.