Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …

Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …

In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …

We propose UTSP, an Unsupervised Learning (UL) framework for solving the Travelling
Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss …

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …

The recent end-to-end neural solvers have shown promise for small-scale routing problems
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …

Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …

Despite the success of neural-based combinatorial optimization methods for end-to-end
heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we …

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …

Machine learning has been adapted to help solve NP-hard combinatorial optimization
problems. One prevalent way is learning to construct solutions by deep neural networks …