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Algorithms
Special Issues
2026 and 2027 Selected Papers from Algorithms Editorial Board Members
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2026 and 2027 Selected Papers from Algorithms Editorial Board Members

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 30 November 2027 | Viewed by 2669

Special Issue Editor

Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, 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,

I am pleased to announce a Special Issue of Algorithms entitled “2026 and 2027 Selected Papers from Algorithms Editorial Board Members”. It will mainly focus on either selected areas of research or particular techniques. With this innovative Special Issue, Algorithms seeks to compile a collection of papers submitted exclusively by its Editorial Board Members (EBMs) covering different areas relating to algorithms and their applications. The main idea behind this Special Issue is to turn the tables and allow our readers to be the judges of our Board Members.

Prof. Dr. Frank Werner
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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

  • algorithm
  • distributed and parallel algorithms
  • computer vision
  • metaheuristics and matheuristics
  • numerical analysis

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Published Papers (5 papers)

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Research

10 pages, 828 KB  
Article
A Novel and Practical Algorithmic Enhancement for Enumerating Maximal and Maximum k-Partite Cliques in k-Partite Graphs
by Cheng Chen, Faisal N. Abu-Khzam, Levente Dojcsak and Michael A. Langston
Algorithms 2026, 19(5), 333; https://doi.org/10.3390/a19050333 - 25 Apr 2026
Viewed by 161
Abstract
A k-partite graph is one whose vertices can be partitioned into k disjoint partite sets, with edges allowed between but not within these sets. In such a graph, a maximal k-partite clique is a subgraph with at least one vertex from [...] Read more.
A k-partite graph is one whose vertices can be partitioned into k disjoint partite sets, with edges allowed between but not within these sets. In such a graph, a maximal k-partite clique is a subgraph with at least one vertex from each partite set and every allowable edge such that the subgraph cannot be enlarged by the incorporation of additional vertices. A maximum k-partite clique is of course a maximal k-partite clique of the greatest size. The results reported here describe a novel and practical modification of the best previously published algorithm for the enumeration of these special subgraphs. The relative performance of this new method relies on implicit edge addition and search tree pruning and is evaluated on graphs constructed from both pseudorandom and real-world data. Full article
(This article belongs to the Special Issue 2026 and 2027 Selected Papers from Algorithms Editorial Board Members)
28 pages, 5387 KB  
Article
Multi-Objective Optimized Differential Privacy with Interpretable Machine Learning for Brain Stroke and Heart Disease Diagnosis
by Mohammed Ibrahim Hussain, Arslan Munir, Safiul Haque Chowdhury, Mohammad Mamun and Muhammad Minoar Hossain
Algorithms 2026, 19(4), 260; https://doi.org/10.3390/a19040260 - 27 Mar 2026
Viewed by 453
Abstract
Brain stroke (BS) and heart disease (HD) are leading causes of global mortality and long-term disability, underscoring the critical need for early and accurate diagnostic tools. This research addresses the dual challenge of developing high-performance predictive models while ensuring the privacy of sensitive [...] Read more.
Brain stroke (BS) and heart disease (HD) are leading causes of global mortality and long-term disability, underscoring the critical need for early and accurate diagnostic tools. This research addresses the dual challenge of developing high-performance predictive models while ensuring the privacy of sensitive patient data. We propose a framework that integrates ensemble machine learning (ML) models with a formal differential privacy (DP) mechanism. Using a dataset of 5110 samples with clinical features, we evaluate Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CAT) for BS and HD prediction. To protect individual privacy, we apply the Gaussian mechanism of DP with two probabilities of failure (POF) parameters (10–5 and 10–6) and a privacy budget ranging from 0.5 to 5.0. A key novelty of this work is the application of Pareto frontier multi-objective optimization (PFMOO) to systematically identify the optimal trade-off between model accuracy and privacy constraints. Our approach successfully identifies optimal, privacy-preserving models: XGB achieves top performance for BS prediction (92.3% accuracy, 92.29% F1 score), with a POF of 10–6, while RF excels for HD detection (95.61% accuracy, 97.8% precision), with a POF of 10–5. Furthermore, we employ explainable AI (XAI) techniques, SHAP and LIME, to provide interpretability of the model decisions, enhancing clinical trust. This research delivers a robust, interpretable, and privacy-conscious framework for early disease detection, offering a significant advancement over existing methods by holistically balancing accuracy, data security, and transparency. Full article
(This article belongs to the Special Issue 2026 and 2027 Selected Papers from Algorithms Editorial Board Members)
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37 pages, 742 KB  
Article
A Life-Cycle Technology Upgrade Scheduling Model
by Massimiliano Caramia
Algorithms 2026, 19(3), 223; https://doi.org/10.3390/a19030223 - 16 Mar 2026
Viewed by 355
Abstract
Technology upgrades are a central lever for sustainability, yet many optimization models primarily account for use-phase emissions and treat embodied impacts and technological change exogenously. We propose a multi-period mixed-integer optimization framework that couples upgrade timing, technology choice, and operations with a life-cycle [...] Read more.
Technology upgrades are a central lever for sustainability, yet many optimization models primarily account for use-phase emissions and treat embodied impacts and technological change exogenously. We propose a multi-period mixed-integer optimization framework that couples upgrade timing, technology choice, and operations with a life-cycle assessment (LCA) structure. The model (i) separates use-phase and embodied impacts at the transition level, (ii) supports time-weighted valuation of impacts through a flexible weighting sequence (time value of carbon), and (iii) incorporates endogenous learning-by-doing that can reduce both investment costs and embodied impacts of future upgrades. We derive an exact Benders (L-shaped) decomposition that separates discrete upgrade dynamics from a linear operating subproblem. Computational experiments illustrate model behavior and report runtimes under an outer-loop implementation with open-source solvers, highlighting that decomposition becomes most beneficial when extensions substantially enlarge the dispatch layer (e.g., scenario expansion). Experiments also show that ignoring embodied impacts can mis-rank upgrade schedules and even violate life-cycle caps, that stronger time-weighting pushes upgrades earlier, and that learning can make staged upgrades economically preferable. Full article
(This article belongs to the Special Issue 2026 and 2027 Selected Papers from Algorithms Editorial Board Members)
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23 pages, 2294 KB  
Article
Electric Load Forecasting for a Quicklime Company Using a Temporal Fusion Transformer
by Jersson X. Leon-Medina, Diego A. Tibaduiza, Claudia Patricia Siachoque Celys, Bernardo Umbarila Suarez and Francesc Pozo
Algorithms 2026, 19(3), 208; https://doi.org/10.3390/a19030208 - 10 Mar 2026
Viewed by 432
Abstract
Accurate short-term electric load forecasting is essential for the operation and management of energy-intensive manufacturing processes such as quicklime production, for which power demand is driven by stage-based operation, fixed schedules, and abrupt load transitions. This study presents a data-driven forecasting framework based [...] Read more.
Accurate short-term electric load forecasting is essential for the operation and management of energy-intensive manufacturing processes such as quicklime production, for which power demand is driven by stage-based operation, fixed schedules, and abrupt load transitions. This study presents a data-driven forecasting framework based on a Temporal Fusion Transformer (TFT) model applied to real industrial measurements collected during 2024 from an operating quicklime production plant. The dataset comprises hourly average power demand records (kW) measured at a plant level, stage-dependent motor operation, and a fixed working schedule from 08:00 to 18:00 (Monday to Friday), with weekends and non-operational hours characterized by near-zero load. Coke consumption during the calcination stage is included as an additional contextual variable. The TFT model is trained for multi-horizon forecasting and provides probabilistic prediction intervals through quantile regression. Weekly evaluations demonstrate that the proposed approach accurately captures start–stop behavior, peak-load periods, and structured inactivity intervals. In addition to point-wise accuracy metrics, cumulative energy is evaluated by integrating hourly power over the forecasting horizon, allowing the assessment of energy preservation at the operational level. The resulting energy deviation reaches 4.78% for the full horizon and 5.25% when restricted to active production hours, confirming strong consistency between predicted and actual cumulative energy. A comparative analysis against LSTM, GRU, and N-BEATS models shows that recurrent architectures achieve lower MAE and RMSE values, while the TFT model delivers superior cumulative energy consistency, highlighting a trade-off between instantaneous accuracy and operational energy fidelity. Overall, the results demonstrate that the proposed TFT-based framework provides a robust and practically relevant solution for short-term industrial electric load forecasting and decision support in stage-driven manufacturing systems under real operating conditions. Full article
(This article belongs to the Special Issue 2026 and 2027 Selected Papers from Algorithms Editorial Board Members)
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25 pages, 766 KB  
Article
An Integrated FAHP-FTOPSIS Algorithm for Evaluating Competencies in Traditional and Agile Project Management: A Case Study in the Automotive Industry
by Marija Savković, Nikola Komatina, Marko Djapan, Dragan Marinković and Arso Vukićević
Algorithms 2026, 19(2), 129; https://doi.org/10.3390/a19020129 - 5 Feb 2026
Viewed by 635
Abstract
In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process [...] Read more.
In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process method extended with triangular fuzzy sets (FAHP) was used to determine the criteria weights applied for ranking the specific competencies within the most important groups. For ranking competencies within these key groups, the Technique for Order Preference by Similarity to Ideal Solution method extended with triangular fuzzy sets (FTOPSIS) was applied. The same algorithmic procedure was carried out for both traditional and agile project management approaches, in a case study conducted across four companies in the automotive industry. The study showed that, in traditional project management, the most important competency group is related to organizational and managerial skills and competencies. On the other hand, in agile project management, the most important competency group refers to contextual skills and competencies. Furthermore, within the traditional approach, the most significant specific competency is project goal orientation, while in the agile approach, the most significant specific competency is customer and stakeholder orientation. Full article
(This article belongs to the Special Issue 2026 and 2027 Selected Papers from Algorithms Editorial Board Members)
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