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🩄NCO-ZooParty

  • Welcome to the NCo-ZooParty.
  • The full name of NCO is Neural Combinatorial Optimization.
  • The repository is devoted to sharing advanced and lasted papers that solve challenging Combinatorial Optimization Problems.

🐯Biography

  • JinkunDong is a third-year PhD student at the Key Laboratory of Big Data & Artificial Intelligence in Transportation, Beijing Jiaotong University.
  • Current research interest is Vehicle Routing Problems.

🩌Neural Combinatorial Optimization(NCO)

🐒Survey

  • 【Survey】Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon∗, Bengio, 2018, European Journal of Operations Research.[Paper]
  • 【Survey】Learning to Solve Vehicle Routing Problems: A Survey, T-ITS, 2022.[Paper]
  • 【Survey】Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives, 2024. [Paper]
  • 【Survey】A Survey on Reinforcement Learning for Combinatorial Optimization, AIC, 2023. [Paper]
  • 【Survey】A Review on Learning to Solve Combinatorial Optimisation Problems in Manufacturing, IET Collaborative Intelligent Manufacturing, 2022. [PDF]

🐈2026

🐣2025

  • Recurrent State Encoders for Efficient Neural Combinatorial Optimization, 2025, arxiv. [Paper][Code]
  • VRPAGENT: LLM-Driven Discovery Of Heuristic Operators For Vehicle Routing Problems, 2025, arxiv. [Paper][Code]
  • 【PARCO】PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization, 2025, Nips.[Paper][Code]
  • Neural Combinatorial Optimization for Time Dependent Traveling Salesman Problem, 2025, Nips.
  • Solving the Asymmetric Traveling Salesman Problem via Trace-Guided Cost Augmentation, 2025, Nips.
  • Large Language Models as End-to-end Combinatorial Optimization Solvers, 2025, Nips. [Paper][Code]
  • UniteFormer: Unifying Node and Edge Modalities in Transformers for Vehicle Routing Problem, 2025, Nips.
  • Probing Neural Combinatorial Optimization Models, 2025, Nips.
  • 【HBG】Hybrid-Balance GFlowNet for Solving Vehicle Routing Problems, 2025, Nips. [Paper][Code]
  • 【L2C-insert】Learning to Insert for Constructive Neural Vehicle Routing Solver, 2025, Nips. [Paper]
  • Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees, 2025, Nips. [Paper]
  • 【TTPL】Improving Generalization of Neural Combinatorial Optimization for Vehicle Routing Problems via Test-Time Projection Learning , 2025, Nips. [Paper]
  • 【POMO+】POMO+: Leveraging starting nodes in POMO for solving Capacitated Vehicle Routing Problem, 2025, arxiv. [Paper]
  • 【L2Seg】Learning to Segment for Vehicle Routing Problems, 2025, arxiv. [Paper]
  • Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems, 2025, arxiv. [Paper]
  • 【MTL-KD】MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver, 2025, arxiv. [Paper]
  • Enhancing Generalization in Large-Scale HCVRP: A Rank-Augmented Neural Solver, 2025, SIGKDD.
  • 【GFACS】Ant Colony Sampling with GFlowNets for Combinatorial Optimization, 2025, arxiv. [Paper][Code]
  • 【GELD】From Global Assessment to Local Selction: Efficiently Solving Traveling Saleman Problems of All Sizes, 2025, arxiv. [Paper][Code]
  • 【RedAHD】RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models, 2025, arxiv. [Paper]
  • A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver, 2025, ICML.
  • 【SHIELD】SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity & Hierarchy in Efficiently Layered Decoder, 2025, IJCAI. [Paper]
  • Preference-based Deep Reinforcement Learning for Historical Route Estimation, 2025, IJCAI.
  • 【DGL】DGL: Dynamic Global-Local Information Aggregation for Scalable VRP Generalization with Self-Improvement Learning, 2025, IJCAI. [Paper]
  • 【EFormer】EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems, 2025, IJCAI. [Paper]
  • 【PO】Preference Optimization for Combinatorial Optimization Problems, 2025, ICML. [Paper]
  • 【TrajEvo】TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution, 2025, arxiv. [Paper][Code]
  • Large Language Models Powered Neural Solvers For Generalized Vehicle Routing Problems, 2025, ICLR. [Paper][Code]
  • 【DRHG】Destroy and Repair Using Hyper-Graphs for Routing, 2025, AAAI. [Paper][Code]
  • TuneNSearch: a hybrid transfer learning and local search approach for solving vehicle routing problems, 2025, arxiv. [Paper]
  • Neural Combinatorial Optimization for Real-World Routing, 2025, arxiv. [paper][Code]
  • 【POCO】Neural Combinatorial Optimization via Preference Optimization, 2025, arxiv. [Paper]
  • 【L2R】L2R: Learning to Reduce Search Space for Generalizable Neural Routing Solver, 2025, arxiv. [Paper]
  • 【U-ML4TSP】UNIFY ML4TSP: DRAWING METHODOLOGICAL PRINCIPLES FOR TSP AND BEYOND FROM STREAMLINED DESIGN SPACE OF LEARNING AND SEARCH, 2025, ICLR. [Paper][Code]
  • 【PoH】Planning of Heuristics: Strategic Planning on Large Language Models with Monte Carlo Tree Search for Automating Heuristic Optimization, 2025, arxiv.[Paper]
  • 【IDEQ】IDEQ: an improved diffusion model for the TSP, 2025, arxiv.[Paper]
  • 【PolyNet】PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization, 2025, ICLR.[Paper][Code]
  • Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search, 2025, arxiv.[Paper]
  • 【SIT】Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems, 2025, ICLR.[Paper][Code]
  • 【LocalEscaper】LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers, 2025, arxiv.[Paper]
  • 【HLGP】Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing Problems, 2025, AAMAS.[Paper][Code]
  • Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems, 2025, TNNLS.[Paper][Code]
  • An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem, 2025, SIGKDD.[Paper][Code]
  • 【AGFN】Adversarial Generative Flow Network for Solving Vehicle Routing Problems, 2025, ICLR.[Paper][Code]
  • 【ReLD】Rethinking Light Decoder-based Solvers for Vehicle Routing Problems, 2025, ICLR.[Paper]
  • 【DualOpt】DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem, 2025, AAAI.[Paper][Code]
  • 【CAMP】CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems, 2025, AAMAS. [Paper][Code]
  • Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning, 2025, arxiv. [Paper]
  • 【NDS】Neural Deconstruction search for Vehicle Routing Problems, 2025, arxiv.[Paper]

🩍2024

  • 【MARCO】MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization, 2024, IJCAI. [Paper][Code]
  • 【LRBS】Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation, 2024, arxiv. [Paper]
  • 【UNCO】UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model, 2024, arxiv.[Paper]
  • 【EoH】Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model, 2024, ICML.[Paper][Code]
  • 【ReEvo】Large Language Models as Hyper-Heuristics for Combinatorial Optimization, 2024, Nips.[Paper][Code]
  • Prompt Learning for Generalized Vehicle Routing, 2024, IJCAI.[Paper][Code]
  • 【Collaboration】Collaboration! Towards Robust Neural Methods for Vehicle Routing Problems, 2024, Nips.[Paper][Code]
  • 【GOAL】GOAL: A Generalist Combinatorial Optimization Agent Learner, 2024, arxiv. [Paper][Code]
  • 【UDC】UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems, 2024, Nips. [Paper][Code]
  • 【DISCO】DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems, 2024, arxiv.[Paper]
  • 【ICAM】Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization, 2024, arxiv. [Paper]
  • 【MEMENTO】Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization, 2024, arxiv. [Paper][Code]
  • 【HNC】Hierarchical Neural Constructive Solver for Real-world TSP Scenarios, 2024, SIGKDD.[Paper]
  • 【GD】Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement, 2024, TMLR.[Paper][Code]
  • 【LR-POMO】Leader Reward for POMO-Based Neural Combinatorial Optimization, 2024, arxiv.[Paper]
  • 【INViT】INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer, 2024, ICML.[Paper][Code]
  • 【GNARKD】Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed, 2024, AAAI.[Paper][Code]
  • An adaptive variable neighborhood search approach for the dynamic vehicle routing problem, 2024, Computers and Operations Research.[Paper]
  • 【GLOP】GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time, 2024, AAAI.[Paper][Code]
  • 【Rl4co】Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark, 2024, ICLR.[Paper][Code]
  • Unsupervised graph neural networks with recurrent features for solving combinatorial optimization problems, 2024, arxiv.[Paper]
  • Train Short, Test Long In Combinatorial Optimization, 2024, ICLR. [Paper]
  • Enhancing Sample Efficiency in Black-box Combinatorial Optimization via Symmetric Replay Training, 2024, arxiv.[Paper]
  • Enhancing the Cross-Size Generalization for Solving Vehicle Routing Problems via Continual Learning, 2024, arxiv.[Paper]
  • EXPLORING BATTERY USAGE IN ELECTRIC VEHICLES THROUGH GRAPH BASED CASCADED CLUSTERING, 2024, ICLR.[Paper]
  • Deep Reinforcement Learning for Dynamic Capacitated Vehicle Routing Problem, 2024, ICLR.[Paper]
  • 【ELG】Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy, 2024, IJCAI.[Paper][Code]
  • 【mEGAT】SYMMETRY-PRESERVING GRAPH ATTENTION NETWORK TO SOLVE ROUTING PROBLEMS AT MULTIPLE RESOLUTIONS, 2024, arxiv. [Paper][Code]
  • Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization, 2024, arxiv.[Paper]
  • 【SIL】Self-Improved Learning for Scalable Neural Combinatorial Optimization, 2024, arxiv.[Paper]
  • 【LCH-Regret】Learning Encodings for Constructive Neural Combinatorial Optimization Needs to Regret, 2024, AAAI.[Paper][Code]
  • 【Moco】MOCO: A Learnable Meta Optimizer for Combinatorial Optimization, 2024, arxiv. [Paper][Code]
  • 【Position】Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems, 2024, ICML. [Paper][Code]
  • 【ESF-DS】Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture, arxiv, 2024. [Paper][Code]
  • 【RouteFinder】RouteFinder: Towards Foundation Models for Vehicle Routing Problems, 2024, ICML.[Paper][Code]
  • 【Step&Reconsider】Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization, 2024, ECAI. [Paper][Code]
  • 【SRT】Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization, 2024, ICML. [Paper][Code]

🐕2023

  • 【IIL】IMITATION IMPROVEMENT LEARNING FOR LARGESCALE CAPACITATED VEHICLE ROUTING PROBLEMS, 2023, ICAPS.[Paper][Code]
  • 【UTSP】Unsupervised Learning for solving the TSP, 2023, NeurIPS.[Paper][Code]
  • 【FER】Learning Feature Embedding Refiner for Solving Vehicle Routing Problems, 2023, TNNLS.[Paper][Code]
  • 【Poppy】Population-based reinforcement learning for combinatorial optimization, 2023, NeurIPS.[Paper][Code]
  • 【COMPASS】Combinatorial Optimization with Policy Adaptation using Latent Space Search, 2023, NeurIPS.[Paper][Code]
  • 【TAM】Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time, 2023, ICLR.[Paper]
  • 【Meta-SAGE】Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization, 2023, ICML.[Paper][Code]
  • DATA-EFFICIENT SUPERVISED LEARNING IS POWERFUL FOR NEURAL COMBINATORIAL OPTIMIZATION, 2023, arxiv.[Paper]
  • 【Omni-VRP】Towards Omni-generalizable Neural Methods for Vehicle Routing Problems, 2023, ICML.[Paper][Code]
  • 【DIFUSCO】DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization, 2023, NeurIPS.[Paper][Code]
  • 【LEHD】Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization, 2023, NeurIPS.[Paper][Code]
  • 【Pointerformer】Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem, 2023, AAAI.[Paper][Code]
  • 【Tspformer】Memory-efficient Transformer-based network model for Traveling Salesman Problem, 2023, Neural Networks.[Paper][Code]
  • 【H-TSP】H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem, 2023, AAAI.[Paper][Code]
  • Efficient graph neural architecture search using Monte Carlo Tree search and prediction network, 2023, ESWA.[Paper][Code]
  • An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Travelling Salesman Problems, 2023, KBS.[Paper]
  • 【HDR】A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem, 2023, arxiv.[Paper]
  • 【SO】Select and Optimize: Learning to solve large-scale TSP instances, 2023, AISTATS.[Paper][Code]
  • Revisiting Sampling for Combinatorial Optimization, 2023, ICML.[Paper]
  • 【ROCO】ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs, 2023, ICLR.[Paper][Code]
  • A GNN-GUIDED PREDICT-AND-SEARCH FRAMEWORK FOR MIXED-INTEGER LINEAR PROGRAMMING, 2023, ICLR.[Paper][Code]
  • 【MVGCL】Multi-View Graph Contrastive Learning for Solving Vehicle Routing Problems, 2023, UAI.[Paper]
  • BOOSTING DIFFERENTIABLE CAUSAL DISCOVERY VIA ADAPTIVE SAMPLE REWEIGHTING, 2023, ICLR.[Paper][Code]
  • 【DeepACO】DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization, 2023, NeurIPS.[Paper][Code]
  • 【BQ-NCO】BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization, 2023, NeurIPS.[Paper][Code]
  • 【RL-CSL】RL-CSL: A Combinatorial Optimization Method Using Reinforcement Learning and Contrastive Self-Supervised Learning, 2023, TETCI.[Paper][Code]
  • 【NeuralGLS】Neuralgls: learning to guide local search with graph convolutional network for the traveling salesman problem, 2023, Neural Computing and Applications.[Paper]
  • 【Cnn-Transformer】A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems, 2023, Applied Intelligence. [PDF][Code]

🩊2022

  • 【SCA】Scale-conditioned Adaptation for Large Scale Combinatorial Optimization, 2022, NeurIPS. [Paper]
  • 【GNNGLS】Graph Neural Network Guided Local Search for the Traveling Salesperson Problem, 2022, ICLR.[Paper][Code]
  • 【Sym-NCO】Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization, 2022, NeurIPS.[Paper][Code]
  • Learning to Solve Routing Problems via Distributionally Robust Optimization, 2022, AAAI.[Paper][Code]
  • 【AMDKD】Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation, 2022, NeurIPS.[Paper][Code]
  • 【DIMES】DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems, 2022, NeurIPS.[Paper][Code]
  • 【EAS】EFFICIENT ACTIVE SEARCH FOR COMBINATORIAL OPTIMIZATION PROBLEMS, 2022, ICLR.[Paper][Code]
  • Graph Neural Network Guided Local Search for the Traveling Salesperson Problem, 2022, ICLR.[Paper][Code]
  • Learning the Travelling Salesperson Problem Requires Rethinking Generalization, 2022, arxiv.[Paper][Code]
  • Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum, 2022, AAAI.[Paper]
  • A GAME-THEORETIC APPROACH FOR IMPROVING GENERALIZATION ABILITY OF TSP SOLVERS, 2022, ICLR.[Paper]
  • 【SGBS】Simulation-guided Beam Search for Neural Combinatorial Optimization, 2022, NeurIPS.[Paper][Code]
  • Large Neighborhood Search based on Neural Construction Heuristics, 2022, arxiv.[Paper][Code]
  • Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem, 2022, Computers & Operations Research.[Paper]
  • 【RBG】Hierarchically Solving Large-Scale Routing Problems in Logistic Systems via Reinforcement Learning, 2022, KDD.[Paper]

đŸ±Earlier

  • 【L2D】Learning to delegate for large-scale vehicle routing, 2021, Nips. [Paper][Code]
  • Erdo ̋s Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs, 2020, Nips. [Paper][Code]
  • Efficiently Solving the Practical Vehicle Routing Problem: A Novel Joint Learning Approach, 2020, KDD. [Paper][Code]
  • 【KGLS】Knowledge-guided local search for the vehicle routing problem, 2019, Computers & Operations Research.[Paper]
  • GENERALIZATION IN DEEP RL FOR TSP PROBLEMS VIA EQUIVARIANCE AND LOCAL SEARCH, 2021, SN Computer Science.[Paper]
  • Learning 2‑Opt Heuristics for Routing Problems via Deep Reinforcement Learning, 2021, SN Computer Science.[Paper][Code]
  • 【SADM】Attention, Filling in The Gaps for Generalization in Routing Problems, 2022, ECML-PKDD.[Paper]
  • 【Att-GCRN】Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances, 2021, AAAI.[Paper][Code]
  • LEARNING A LATENT SEARCH SPACE FOR ROUTING PROBLEMS USING VARIATIONAL AUTOENCODERS, 2021, ICLR.[Paper][Code]
  • Deep Policy Dynamic Programming for Vehicle Routing Problems, 2021.[Paper][Code]
  • 【NeuroLKH】NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem, 2021, NeurIPS.[Paper][Code]
  • 【LCP】Learning Collaborative Policies to Solve NP-hard Routing Problems, 2021, NeurIPS.[Paper][Code]
  • 【DACT】Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer, 2021, NeurIPS.[Paper][Code]
  • 【MatNet】Matrix Encoding Networks for Neural Combinatorial Optimization, 2021, NeurIPS.[Paper][Code]
  • 【Ptr-Net】Pointer Networks, 2017, NeurIPS.[Paper][Code]
  • 【GCN】An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem, 2019, INFORMS.[Paper][Code]
  • 【AM】ATTENTION, LEARN TO SOLVE ROUTING PROBLEMS!, 2019, ICLR.[Paper][Code]
  • 【POMO】POMO: Policy Optimization with Multiple Optima for Reinforcement Learning, 2020, NeurIPS.[Paper][Code]
  • Reinforcement Learning for Solving the Vehicle Routing Problem, 2018, NeurIPS.[Paper][Code]
  • NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING, 2017, ICLR.[Paper][Code]
  • Knowledge-guided local search for the vehicle routing problem, 2019, Computers & Operations Research.[Paper]
  • Combinatorial optimization by graph pointer networks and hierarchical reinforcement learning, 2019, arxiv.[Paper][Code]
  • 【MDAM】Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems, 2020, AAAI.[Paper][Code]
  • Step-wise Deep Learning Models for Solving Routing Problems, 2020, TII.[Paper][Code]
  • 【AMD】A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems, 2022, ISICA.[Paper][Code]

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