Zhiguang Cao

19

Papers

92

Total Citations

Papers (19)

GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time

ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning

Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling

SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy

DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem

Adaptive Stabilization Based on Machine Learning for Column Generation

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt

Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift

Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

Learning Large Neighborhood Search Policy for Integer Programming

Graph Learning Assisted Multi-Objective Integer Programming

Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation