IDEQ: an improved diffusion model for the TSP

TL;DR

IDEQ is a novel diffusion-based approach that significantly improves solution quality for the Traveling Salesman Problem, outperforming existing neural methods and matching or surpassing traditional heuristics on benchmark instances.

Contribution

We introduce IDEQ, an improved diffusion model that leverages TSP structure and curriculum learning to achieve state-of-the-art results in neural network-based TSP solutions.

Findings

IDEQ achieves 0.3% optimality gap on 500-city instances.

IDEQ outperforms DIFUSCO and T2TCO in scalability and variance.

IDEQ matches or exceeds LKH3 on TSPlib benchmarks.

Abstract

We investigate diffusion models to solve the Traveling Salesman Problem. Building on the recent DIFUSCO and T2TCO approaches, we propose IDEQ. IDEQ improves the quality of the solutions by leveraging the constrained structure of the state space of the TSP. Another key component of IDEQ consists in replacing the last stages of DIFUSCO curriculum learning by considering a uniform distribution over the Hamiltonian tours whose orbits by the 2-opt operator converge to the optimal solution as the training objective. Our experiments show that IDEQ improves the state of the art for such neural network based techniques on synthetic instances. More importantly, our experiments show that IDEQ performs very well on the instances of the TSPlib, a reference benchmark in the TSP community: it closely matches the performance of the best heuristics, LKH3, being even able to obtain better solutions than LKH3 on 2 instances of the TSPlib defined on 1577 and 3795 cities. IDEQ obtains 0.3% optimality gap on TSP instances made of 500 cities, and 0.5% on TSP instances with 1000 cities. This sets a new SOTA for neural based methods solving the TSP. Moreover, IDEQ exhibits a lower variance and better scales-up with the number of cities with regards to DIFUSCO and T2TCO.