mlsanityData to 14 Oct 2026

NeuFACO: Neural Focused Ant Colony Optimization for Traveling Salesman Problem

arXiv 2509.16938 ↗First posted 21 Sep 2025cs.NE cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

This study presents Neural Focused Ant Colony Optimization (NeuFACO), a non-autoregressive framework for the Traveling Salesman Problem (TSP) that combines advanced reinforcement learning with enhanced Ant Colony Optimization (ACO). NeuFACO employs Proximal Policy Optimization (PPO) with entropy regularization to train a graph neural network for instance-specific heuristic guidance, which is integrated into an optimized ACO framework featuring candidate lists, restricted tour refinement, and scalable local search. By leveraging amortized inference alongside ACO stochastic exploration, NeuFACO efficiently produces high-quality solutions across diverse TSP instances.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 37 references that are in the map. First posted on arXiv 21 Sep 2025.

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15 of 37 references.

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  1. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement22 Mar
  2. 2024INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer4 Feb

Other references · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  3. 2015Pointer Networkscited by 895
  4. 2018Attention, Learn to Solve Routing Problems!cited by 656
  5. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  6. 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 142
  7. 2023GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-timecited by 85
  8. 2023BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimizationcited by 82
  9. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 71
  10. 2024Ant Colony Sampling with GFlowNets for Combinatorial Optimizationcited by 53
  11. 2023Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problemcited by 50
  12. 2023H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problemcited by 43
1 more
  1. 2022Neural Combinatorial Optimization: a New Player in the Fieldcited by 6

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  1. 2025Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
  2. 2025An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem
  3. 2024Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization
  4. 2026Decoupling Numerical and Structural Parameters: An Empirical Study on Adaptive Genetic Algorithms via Deep Reinforcement Learning for the Large-Scale TSP
  5. 2025BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization
  6. 2026Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem
  7. 2025LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers
  8. 2026GeoRouteNet: A Geometry-Aware Non-Autoregressive Neural Solver for the Euclidean Traveling Salesman Problem