Neural combinatorial optimization: A tutorial
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...
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| Vydáno v: | Computers & operations research Ročník 182; s. 107102 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.10.2025
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| ISSN: | 0305-0548 |
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| 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.
•We provide a tutorial on NCO.•We give an example on the TSP and the KP.•We provide a short code for a basic version of NCO.•We discuss some pros and cons of NCO. |
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| AbstractList | 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.
•We provide a tutorial on NCO.•We give an example on the TSP and the KP.•We provide a short code for a basic version of NCO.•We discuss some pros and cons of NCO. |
| ArticleNumber | 107102 |
| Author | Angioni, Davide Archetti, Claudia Speranza, M. Grazia |
| Author_xml | – sequence: 1 givenname: Davide orcidid: 0000-0002-8825-7817 surname: Angioni fullname: Angioni, Davide email: d.angioni@unibs.it, davide.angioni@orobix.com organization: Department of Economics and Management, University of Brescia, Italy – sequence: 2 givenname: Claudia orcidid: 0000-0002-3524-1600 surname: Archetti fullname: Archetti, Claudia organization: Department of Economics and Management, University of Brescia, Italy – sequence: 3 givenname: M. Grazia orcidid: 0000-0002-8893-5227 surname: Speranza fullname: Speranza, M. Grazia email: grazia.speranza@unibs.it organization: Department of Economics and Management, University of Brescia, Italy |
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| Keywords | Traveling salesman problem Deep neural networks Neural Combinatorial Optimization Reinforcement learning |
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