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.

翻译：蚁群优化（ACO）是一种元启发式算法，已成功应用于多种组合优化问题（COPs）。传统上，针对特定问题定制ACO需要专家设计基于知识的启发式方法。本文提出DeepACO——一个利用深度强化学习自动化启发式设计的通用框架。DeepACO能够增强现有ACO算法的启发式度量，并免除未来ACO应用中繁琐的手工设计。作为一种神经增强型元启发式算法，DeepACO在八个组合优化问题上使用单一神经网络模型和单一超参数集，始终优于其对应的ACO算法。作为一种神经组合优化方法，DeepACO在规范路由问题上的表现优于或持平于专用方法。我们的代码已开源在https://github.com/henry-yeh/DeepACO。