DeepACO: neural-enhanced ant systems for combinatorial optimization

• Main Authors: YE, Haoran, WANG, Jiarui, CAO, Zhiguang, LIANG, Helan, LI, Yong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8401; https://ink.library.smu.edu.sg/context/sis_research/article/9404/viewcontent/2309.14032.pdf
• Institution: Singapore Management University

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 leveraging 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 (NCO) method, DeepACO also performs better than or competitively against the problem-specific methods on the canonical Travelling Salesman Problem.