Work

Neural combinatorial optimization: A tutorial

Year: 2025

Type: article

Abstract: Recent advancements in deep reinforcement learning have sparked a growing interest in the application of this approach to solve combinatorial optimization (CO) problems. This paper presents neural combinatorial optimization (NCO) as a framework for constructing functions that work as heuristics for CO problems. Given the rapid expansion of the field and the increasing interest in the topic, this tutorial introduces the main techniques utilized in NCO and explores the current open issues in the field. We define key terms and concepts related to NCO and present the latest developments, using the Knapsack Problem as a running example to complement theoretical explanations. Finally, we analyze prominent works in the field of NCO, with a focus on their application to the Traveling Salesman Problem , which serves as the most extensively studied problem in this domain.

Source: Computers & Operations Research

DOI: 10.1016/j.cor.2025.107102

Authors: Davide Angioni, Claudia Archetti, M. Grazia Speranza

Institution: University of Brescia

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,570

OpenAlex ID: W4410610627

FWCI: 2.855

Cites: 65

Cited by: 7

Related to: 10