DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li

Keywords: Neural Combinatorial Optimization, Ant Colony Optimization, Evolutionary algorithm, Meta-heuristic, Deep reinforcement learning, Learned heuristic measure, Neural local search, Generalization

TL;DR: We propose a neural-enhanced version of Ant Colony Optimization meta-heuristic for Combinatorial Optimization.

Abstract: 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 model 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.

Submission Number: 7805