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Algorithms
Special Issues
Engineering Applications of Optimization Algorithms: Heuristics, Metaheuristics, and Hyperheuristics
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Engineering Applications of Optimization Algorithms: Heuristics, Metaheuristics, and Hyperheuristics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2420

Special Issue Editors

Dr. Mohammad Shokouhifar

E-Mail Website
Guest Editor
DTU AI and Data Science Hub (DAIDASH), Duy Tan University, Da Nang 550000, Vietnam
Interests: artificial intelligence; optimization algorithms; biomedical engineering; remote healthcare monitoring; prognosis and diagnosis; machine learning; deep learning; swarm and evolutionary algorithms; hyper-heuristic algorithms; biomedical image processing; biomedical signal processing; time-series analysis; internet-of-things; wireless body sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Mehdi Hosseinzadeh

E-Mail Website
Guest Editor
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Interests: optimization; healthcare systems; metaheuristics; machine learning; engineering problems
Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling; development of exact and approximate algorithms; stability investigations; discrete optimization; scheduling with interval processing times; complex investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation; applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimization algorithms are powerful tools for solving a wide range of complex real-world problems across various engineering domains. While exact search methods guarantee an optimal solution, their practicality is often limited when dealing with NP-complete/NP-hard problems due to time constraints. In such cases, heuristic, metaheuristic, and hyperheuristic algorithms become crucial, as they offer near-optimal solutions within a reasonable timeframe, balancing complexity and efficiency. The selection of these algorithms is generally guided by the specific application requirements, including the time available and the desired level of accuracy.

The aim of this Special Issue is to bring together advanced research and innovative applications of heuristics, metaheuristics, and hyperheuristics to address complex optimization problems in engineering. We invite contributions from experts in both academia and industry to showcase recent advancements in optimization algorithms and their applications in solving real-world challenges across diverse engineering fields, including computer, electrical, mechanical, chemical, biomedical, civil, and industrial engineering.

Dr. Mohammad Shokouhifar
Dr. Mehdi Hosseinzadeh
Prof. Dr. Frank Werner
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 together 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. Algorithms is an international peer-reviewed open access monthly 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 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • engineering optimization problems
  • heuristic algorithms
  • single-solution metaheuristic algorithms
  • population-based metaheuristic algorithms
  • multi-objective metaheuristic algorithms
  • knowledge-based metaheuristic algorithms
  • heuristic-driven metaheuristic algorithms
  • fuzzy-driven metaheuristic algorithms
  • machine learning-driven metaheuristic algorithms
  • hyperheuristic algorithms for just-in-time (JIT) problems
  • hybrid algorithm design methods

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Published Papers (2 papers)

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Research

28 pages, 2303 KiB  
Article
DLMinTC+: A Deep Learning Based Algorithm for Minimum Timeline Cover on Temporal Graphs
by Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni and Italo Zoppis
Algorithms 2025, 18(2), 113; https://doi.org/10.3390/a18020113 - 17 Feb 2025
Viewed by 469
Abstract
Combinatorial optimization on temporal graphs is critical for summarizing dynamic networks in various fields, including transportation, social networks, and biology. Among these problems, the Minimum Timeline Cover (MinTCover) problem, aimed at identifying minimal activity intervals for representing temporal interactions, remains underexplored in the [...] Read more.
Combinatorial optimization on temporal graphs is critical for summarizing dynamic networks in various fields, including transportation, social networks, and biology. Among these problems, the Minimum Timeline Cover (MinTCover) problem, aimed at identifying minimal activity intervals for representing temporal interactions, remains underexplored in the context of advanced machine learning techniques. Existing heuristic and approximate methods, while effective in certain scenarios, struggle with capturing complex temporal dependencies and scalability in dense, large-scale networks. Addressing this gap, this paper introduces DLMinTC+, a novel deep learning-based algorithm for solving the MinTCover problem. The proposed method integrates Graph Neural Networks for structural embedding, Transformer-based temporal encoding, and Pointer Networks for activity interval selection, coupled with an iterative adjustment algorithm to ensure valid solutions. Key contributions include (i) demonstrating the efficacy of deep learning for temporal combinatorial optimization, achieving superior accuracy and efficiency over state-of-the-art heuristics, and (ii) advancing the analysis of temporal knowledge graphs by incorporating robust, time-sensitive embeddings. Extensive evaluations on synthetic and real-world datasets highlight DLMinTC+’s ability to achieve significant coverage size reduction while maintaining generalization, offering a scalable and precise solution for complex temporal networks. Full article
(This article belongs to the Special Issue Engineering Applications of Optimization Algorithms: Heuristics, Metaheuristics, and Hyperheuristics)
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18 pages, 13100 KiB  
Article
Enhancing Hydraulic Efficiency of Pelton Turbines Through Computational Fluid Dynamics and Metaheuristic Optimization
by Guillermo Barragan, Sebastian Atarihuana, Edgar Cando and Victor Hidalgo
Algorithms 2025, 18(1), 35; https://doi.org/10.3390/a18010035 - 9 Jan 2025
Viewed by 1042
Abstract
In this work, the NSGA-II multi objective genetic algorithm, numerical methods, and parametric design techniques found in the Autodesk Inventor professional 2023 CAD software were combined to perform the geometrical optimization of the Pelton bucket geometry. The validation of the proposed method was [...] Read more.
In this work, the NSGA-II multi objective genetic algorithm, numerical methods, and parametric design techniques found in the Autodesk Inventor professional 2023 CAD software were combined to perform the geometrical optimization of the Pelton bucket geometry. The validation of the proposed method was carried out with numerical simulations using the OpenFOAM CFD program and taking into account the case study turbine’s operating conditions, as well as the k-SST turbulence model. The CFD simulation results and operational data from the case study turbine from the “Illuchi N°2” hydrocenter have been compared in order to validate the proposed methodology. The implementation of the NSGA-II in the design process resulted in optimized bucket geometrical parameters: bucket length, width, inlet angle, and outlet angle. These parameters not only resulted in a 2.56% increase in hydraulic efficiency, but also led to a 0.1 [kPa] reduction in the maximum pressure at the bottom of the bucket. Further research will involve testing these parameters using 3D printing methods to validate their effectiveness. Full article
(This article belongs to the Special Issue Engineering Applications of Optimization Algorithms: Heuristics, Metaheuristics, and Hyperheuristics)
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