Neural combinatorial optimization (NCO) is a promising learning-based approach for solving
challenging combinatorial optimization problems without specialized algorithm design by …

Traditional solvers for tackling combinatorial optimization (CO) problems are usually
designed by human experts. Recently, there has been a surge of interest in utilizing deep …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

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 …

Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (ie,
DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is …

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 …

For NP-hard combinatorial optimization problems, it is usually challenging to find high-
quality solutions in polynomial time. Designing either an exact algorithm or an approximate …

Applying machine learning to combinatorial optimization problems has the potential to
improve both efficiency and accuracy. However, existing learning-based solvers often …

Reinforcement learning has recently shown promise in learning quality solutions in many
combinatorial optimization problems. In particular, the attention-based encoder-decoder …

Travelling salesman problem (TSP) is NP-Hard with exponential search space. Recently, the
adoption of encoder-decoder models as neural TSP solvers has emerged as an attractive …