Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have
focused on learning construction heuristics. Such approaches find TSP solutions of good
quality but require additional procedures such as beam search and sampling to improve
solutions and achieve state-of-the-art performance. However, few studies have focused on
improvement heuristics, where a given solution is improved until reaching a near-optimal
one. In this work, we propose to learn a local search heuristic based on 2-opt operators via …

Traveling salesman problem (TSP) is a classical combinatorial optimization problem. As it
represents a large number of important practical problems, it has received extensive studies
and a great variety of algorithms have been proposed to solve it, including exact and
heuristic algorithms. The success of heuristic algorithms relies heavily on the design of
powerful heuristic rules, and most of the existing heuristic rules were manually designed by
experienced experts to model their insights and observations on TSP instances and …