DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

2023/09/25 by Haoran Ye, Jiarui Wang, Ye, Haoran +7 · 20 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Robotic Path Planning Algorithms #Vehicle Routing Optimization Methods

paper · pdf · doi:10.48550/arxiv.2309.14032

openalex publication_date 2023/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 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.

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• On Distributional Dependent Performance of Classical and Neural Routing Solvers
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• RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
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• Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning

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