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.

翻译：蚁群优化（ACO）是一种已成功应用于多种组合优化问题（COP）的元启发式算法。传统上，针对特定问题定制ACO需要专家设计知识驱动的启发式规则。本文提出DeepACO——一种利用深度强化学习自动实现启发式设计的通用框架。该框架既能增强现有ACO算法的启发式度量，又可在未来ACO应用中免除繁复的人工设计。作为神经增强型元启发式方法，DeepACO采用单一神经网络架构与单组超参数，在八类COP问题上持续超越传统ACO算法。作为神经组合优化方法，DeepACO在经典路径规划问题上的性能优于或持平于专用方法。相关代码已开源至https://github.com/henry-yeh/DeepACO。