AJCAI2023 workshop: Machine Learning for Data-driven Optimization

The Neural Combinatorial Optimization Library (NCOLib) is an accessible software library designed to simplify the application of neural network models and deep learning algorithms to solve combinatorial optimization problems.

Unofficial implemnetation of "Solving Quadratic Assignemt Problem using Deep Reinforcement Learning" (https://arxiv.org/abs/2310.01604)

EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding

A peper list for machine learning models solving combinatorial problems, NP-hard problems and problems in graphs.

Official implementation of IJCAI'24 paper "Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy"

[ICML 2023] Official code for "DevFormer: A Symmetric Transformer for Context-Aware Device Placement"

[ICML 2024] "MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts"

This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.

L2O/NCO codes from CIAM Group at SUSTech, Shenzhen, China

The implementation code of our paper "Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation", accepted at NeurIPS2022.

[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time

This repo implements our paper, "Efficient Neural Neighborhood Search for Pickup and Delivery Problems", which has been accepted as short oral at IJCAI 2022.

Large Language Models as Hyper-Heuristics for Combinatorial Optimization (CO)

Recent research papers about Foundation Models for Combinatorial Optimization

This repo implements our paper, "Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer", which has been accepted at NeurIPS 2021.

[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper