Special Issue: “Machine Learning, Metaheuristics and Combinatorial Optimization Problems”






Mathematics (ISSN 2227-7390) is a peer-reviewed open-access journal that provides an advanced forum for studies related to mathematics and is published monthly online by MDPI.

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Special Issue “Machine Learning, Metaheuristics and Combinatorial Optimization Problems”

Deadline for manuscript submissions: 10 February 2023.

Special Issue Editors

Prof. Dr. Víctor Yepes E-Mail Website SciProfiles Guest Editor
Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
Interests: multiobjective optimization; structures optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Special Issues, Collections and Topics in MDPI journals
Dr. José Antonio García E-Mail Website Guest Editor
Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Interests: optimization; deep learning; operations research; artificial intelligence applications to industrial problems
Special Issues, Collections and Topics in MDPI journals
Dr. Broderick Crawford E-Mail Website SciProfiles Guest Editor
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
Interests: information systems; management information systems; operations research; constraint satisfaction problems; collaboration of solvers

Special Issue Information

Dear Colleagues,

Complex combinatorial problems have been successfully addressed through metaheuristic techniques. However, as the size of the problem increases, so does the need for robust optimization algorithms. An interesting method of strengthening these algorithms is through the application of hybrid techniques, specifically the hybridization of machine learning and metaheuristics. We invite researchers to submit articles on combined optimization and hybrid techniques for this Special Issue. Benchmarking problems or applications in the industry are also of interest.

The areas of machine learning and data science have received considerable research interest in recent years. These techniques have strongly excelled in supporting decision-making in complex and data-intensive scenarios. In this Special Issue, we are additionally interested in contributions to machine learning applications in the industry.

Prof. Víctor Yepes
Dr. José Antonio García
Dr. Broderick Crawford
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title, and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service before publication or during author revisions.