🦄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 (TSP, CVRP, PCTSP, CVRPTW...).
Research philosophy is to conduct some simple and effective studies that are meaningful to the current field of research.

🦌Neural Combinatorial Optimization(NCO)

PaperList by Learning Paradigm: PaperList
PaperList by Task: PaperList
Lab: LabList

🐒Survey

【Survey】Survey on neural routing solvers, 2026. [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】Learning to Solve Vehicle Routing Problems: A Survey, T-ITS, 2022.[Paper]
【Survey】A Review on Learning to Solve Combinatorial Optimisation Problems in Manufacturing, IET Collaborative Intelligent Manufacturing, 2022. [Paper]
【Survey】Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon∗, EJOR, 2018.[Paper]

🐈2026

RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization, 2026, Preprint.[Paper]
Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems, 2026, Preprint.[Paper]
Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement, 2026, Preprint.[Paper][Code]
Graph neural networks and the energetic cavity method for combinatorial optimization, 2026, Preprint. [Paper][Code]
The Time-Dependent Traveling Salesman Problem with Loose Time Windows, 2026, Preprint. [Paper]
GNN-Guided Graph Coarsening and Adaptive QUBO Penalties for the Capacitated Vehicle Routing Problem with Time Windows on a Quantum Annealer, 2026, Preprint. [Paper]
Information-theoretic formulation of the Traveling Salesman Problem, 2026, Preprint. [Paper]
LLM-Driven Joint Evolution of Coupled Heuristics Components for Routing Optimization, 2026, Preprint. [Paper]
ES-AHD: An Evolution Strategy Framework for Automatic Heuristic Design, 2026,Preprint. [Paper]
HyCO: A Hybrid Neural Solver for Combinatorial Optimization, 2026,Preprint. [Paper]
【ASAP】ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization, 2026, ICML. [Paper]
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization, 2026, ICML. [Paper][Code]
Unsupervised Diffusion for Combinatorial Optimization via Adjoint Matching, 2026, ICML. [Paper][Code]
Latent Guided Sampling for Combinatorial Optimization, 2026, ICML. [Paper][Code]
Position: Neural Approximation Is Rarely Justified for Hard Combinatorial Problems, 2026, ICML. [Paper][Code]
URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization, 2026, ICML. [Paper][Code]
PoMtVRS: Preference-Optimized Multi-Task Vehicle Routing Solver with Preference Gating, 2026, ICML. [Paper][Code]
【L2R】L2R: Learning to Reduce Search Space for Generalizable Neural Routing Solver, 2026, SIGKDD. [Paper]
【AGDN】AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network, 2026, SIGKDD. [Paper][Code]
Learning to Handle Constrained Routing Problems From a Decoupling Perspective, 2026, SIGKDD. [Paper][Code]
Efficient decoder scaling strategy for neural routing solvers, 2026, arxiv. [Paper]
Constraints matrix diffusion based generative neural solver for vehicle routing problems, 2026, arxiv. [Paper]
【G-LNS】G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design, 2026, arxiv. [Paper][Code]
Refining Hybrid Genetic Search for CVRP via Reinforcement Learning-Finetuned LLM, 2026, ICLR. [Paper]
【EGAM】EGAM: Extended Graph Attention Model for Solving Routing Problems, 2026, arxiv. [Paper]
【GMS】Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs, 2026, ICLR. [Paper]
【MaskCO】MaskCO: Masked Generation Drives Effective Representation Learning and Exploiting for Combinatorial Optimization, 2026, ICLR. [Paper][Code]
Gelato: Graph Edit Distance via Autoregressive Neural Combinatorial Optimization, 2026, ICLR. [Paper]
From Sequential to Parallel: Reformulating Dynamic Programming as GPU Kernels for Large-Scale Stochastic Combinatorial Optimization, 2026, ICLR.
ConRep4CO: Contrastive Representation Learning of Combinatorial Optimization Instances across Types, 2026, ICLR. [Paper]
FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization, 2026, ICLR. [Paper]
Multi-Action Self-Improvement For Neural Combinatorial Optimization, 2026, ICLR. [Paper]
Representation Learning and Exploiting for Combinatorial Optimization, 2026, ICLR. [Paper]
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization, 2026, ICLR. [Paper]
Native Adaptive Solution Expansion for Diffusion-based Combinatorial Optimization, 2026, ICLR. [Paper]
ViTSP: A Vision Language Models Guided Framework for Large-Scale Traveling Salesman Problems, 2026, ICLR. [Paper]
Generalizable Heuristic Generation Through LLMs with Meta-Optimization, 2026, ICLR. [Paper]
【RRNCO】Towards Real-World Routing with Neural Combinatorial Optimization, 2026, ICLR. [Paper][Code]
【CaR】Towards Efficient Constraint Handling in Neural Solvers for Routing Problems, 2026, ICLR. [Paper][Code]
Chain-of-Context Learning: Dynamic Constraint Understanding for Multi-Task VRPs, 2026, ICLR. [Paper]
【RADAR】RADAR: Learning to Route with Asymmetry-aware Distance Representations, 2026, ICLR. [Paper]
Combination-of-Experts with Knowledge Sharing for Cross-Task Vehicle Routing Problems, 2026, ICLR. [Paper]
【AFL】An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems, 2026, ICLR. [Paper][Code]
【L2Seg】Learning to Segment for Vehicle Routing Problems, 2026, ICLR. [Paper]
【DRAGON】DRAGON: LLM-Driven Decomposition and Reconstruction Agents for Large-Scale Combinatorial Optimization, 2026, AAMAS. [Paper][Code]
【LLMAide】LLMAide: Language-Assisted Neural Solver for Vehicle Routing Problems, 2026, AAMAS.
【PyVRP+】PyVRP+: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems, 2026, AAMAS.
【EPR】Elite Pattern Reinforcement for Vehicle Routing Problems, 2026, AAAI. [Paper][Code]
【EvoReal】Bridging Synthetic and Real Routing Problems via LLM-Guided Instance Generation and Progressive Adaptation, 2026, AAAI. [Paper][Code]
【EoH-S】EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design, 2026, AAAI. [Paper][Code]
【Scale-Net】Scale-Net: A Hierarchical U-Net Framework for Cross-Scale Generalization in Multi-Task Vehicle Routing, 2026, AAAI. [Paper][Code]

🐣2025

Learning to Cut Generation in Branch-and-Cut algorithms for Combinatorial Optimization, 2025, TELO. [Paper]
【COExpander】COExpander: Adaptive Solution Expansion for Combinatorial Optimization, 2025, ICML. [Paper][Code]
【DRI】Decompose-route-improve framework for solving large-scale vehicle routing problems with time windows, 2025, TR-E. [Paper]
【ARS】ARS: Automatic Routing Solver with Large Language Models, 2025, arxiv. [Paper][Code]
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design, 2025, Nips. [Paper][Code]
Recurrent State Encoders for Efficient Neural Combinatorial Optimization, 2025, arxiv. [Paper][Code]
【VRPAGENT】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. [Paper][Code]
【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][Code]
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees, 2025, Nips. [Paper][Code]
【TTPL】Improving Generalization of Neural Combinatorial Optimization for Vehicle Routing Problems via Test-Time Projection Learning , 2025, Nips. [Paper][Code]
【MTL-KD】MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver, 2025, Nips. [Paper][Code]
【POMO+】POMO+: Leveraging starting nodes in POMO for solving Capacitated Vehicle Routing Problem, 2025, arxiv. [Paper]
Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems, 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][Code]
【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]
【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][Code]
【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, TMLR.[Paper][Code]

🦍2024

【PIP】Learning to handle complex constraints for vehicle routing problems, 2024, NIPs. [Paper][Code]
【MVMoE】MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts, 2024, ICML. [Paper][Code]
【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][Code]
【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

【NRR】Too big, so fail? -- enabling neural construction methods to solve large-scale routing problems, 2023, arxiv. [Paper][Code]
【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]

🐱Earlier

【MatNet】Matrix Encoding Networks for Neural Combinatorial Optimization, 2021, NeurIPS. [Paper][Code]
【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]
【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]

🐶Contact me

📧email： jinkundong@bjtu.edu.cn
🤙blog： JKDong's Blog

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blog		blog
codeParsing/Att_GCRN		codeParsing/Att_GCRN
Experts4COP.md		Experts4COP.md
PK_Model_Details.md		PK_Model_Details.md
Paper_by_learning_paradigms.md		Paper_by_learning_paradigms.md
Paper_by_task.md		Paper_by_task.md
Ranking.md		Ranking.md
readme.md		readme.md

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