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Simple Rules of a Discrete Stochastic Process Leading to Catalan-like Recurrences
Control of High-Power Slip Ring Induction Generator Wind Turbines at Variable Wind Speeds in Optimal and Reliable Modes
GATransformer: A Graph Attention Network-Based Transformer Model to Generate Explainable Attentions for Brain Tumor Detection
Broadcasting in Stars of Cliques and Path-Connected Cliques

Journal Description

Algorithms

Algorithms is a peer-reviewed, open access journal which provides an advanced forum for studies related to algorithms and their applications. Algorithms is published monthly online by MDPI. The European Society for Fuzzy Logic and Technology (EUSFLAT) is affiliated with Algorithms and their members receive discounts on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Theory and Methods) / CiteScore - Q1 (Numerical Analysis)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.9 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the second half of 2024).
Testimonials: See what our editors and authors say about Algorithms.
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.8 (2023); 5-Year Impact Factor: 1.9 (2023)

Imprint Information Journal Flyer Open Access ISSN: 1999-4893

Latest Articles

17 pages, 1138 KiB

Open AccessArticle

Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions

by Erind Bedalli, Shkelqim Hajrulla, Rexhep Rada and Robert Kosova

Algorithms 2025, 18(6), 327; https://doi.org/10.3390/a18060327 - 29 May 2025

Fuzzy clustering is a form of unsupervised learning that assigns the elements of a dataset into multiple clusters with varying degrees of membership rather than assigning them to a single cluster. The classical Fuzzy C-Means algorithm operates as an iterative procedure that minimizes [...] Read more.

Fuzzy clustering is a form of unsupervised learning that assigns the elements of a dataset into multiple clusters with varying degrees of membership rather than assigning them to a single cluster. The classical Fuzzy C-Means algorithm operates as an iterative procedure that minimizes an objective function defined based on the weighted distance between each point and the cluster centers. The algorithm operates decently in many datasets but struggles with datasets that exhibit irregularities in overlapping shapes, densities, and sizes of clusters. Meanwhile, there is a growing demand for accurate and scalable clustering techniques, especially in high-dimensional data analysis. This research work aims to address these infirmities of the classical fuzzy clustering algorithm by applying several modification approaches on the objective function of this algorithm. These modifications include several regularization terms aiming to make the algorithm more robust in specific types of datasets. The optimization of the modified objective functions is handled based on several numerical methods: gradient descent, root mean square propagation (RMSprop), and adaptive mean estimation (Adam). These methods are implemented in a Python environment, and extensive experimental studies are conducted, following carefully the steps of dataset selection, algorithm implementation, hyper-parameter tuning, picking the evaluation metrics, and analyzing the results. A comparison of the features of these algorithms on various datasets is carefully summarized. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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21 pages, 5140 KiB

Open AccessArticle

Masked Convolutions Within Skip Connections for Video Anomaly Detection

by Demetris Lappas, Vasileios Argyriou and Dimitrios Makris

Algorithms 2025, 18(6), 326; https://doi.org/10.3390/a18060326 - 29 May 2025

Video anomaly detection plays a crucial role in various fields such as surveillance, health monitoring, and industrial quality control. This research paper introduces a novel contribution to the field by presenting MaskedConv3D layers within a modified UNet architecture. These MaskedConv3D layers provide a [...] Read more.

Video anomaly detection plays a crucial role in various fields such as surveillance, health monitoring, and industrial quality control. This research paper introduces a novel contribution to the field by presenting MaskedConv3D layers within a modified UNet architecture. These MaskedConv3D layers provide a unique approach to information propagation in three-dimensional video data by selectively masking temporal regions of convolutional kernels. By incorporating these layers into the skip connections of the UNet, the model gains the ability to infer missing information in the temporal domain based on the surrounding context. This innovative mechanism enhances the preservation of spatial and temporal details, addressing the challenge of effectively detecting anomalies in video data. The proposed methodology is evaluated on popular video datasets, showcasing its effectiveness in capturing intricate patterns and contexts. The results highlight the superiority of the modified UNet with MaskedConv3D layers compared to traditional approaches. Overall, this research introduces a novel technique for information propagation in video data and demonstrates its potential for advancing video anomaly detection. Full article

(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)

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27 pages, 2928 KiB

Open AccessArticle

ML-RASPF: A Machine Learning-Based Rate-Adaptive Framework for Dynamic Resource Allocation in Smart Healthcare IoT

by Wajid Rafique

Algorithms 2025, 18(6), 325; https://doi.org/10.3390/a18060325 - 29 May 2025

The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable [...] Read more.

The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist–edge–cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines. Full article

(This article belongs to the Special Issue AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and Society)

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21 pages, 1005 KiB

Open AccessArticle

Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU

by Jonathan Decker, Vincent Florens Hasse and Julian Kunkel

Algorithms 2025, 18(6), 324; https://doi.org/10.3390/a18060324 - 29 May 2025

Kubernetes has emerged as the industry standard for container orchestration in cloud environments, with its scheduler dynamically placing container instances across cluster nodes based on predefined rules and algorithms. Various efforts have been made to extend and improve upon the Kubernetes scheduler. However, [...] Read more.

Kubernetes has emerged as the industry standard for container orchestration in cloud environments, with its scheduler dynamically placing container instances across cluster nodes based on predefined rules and algorithms. Various efforts have been made to extend and improve upon the Kubernetes scheduler. However, as the majority of Kubernetes clusters operate on homogeneous hardware, most scheduling algorithms are also only developed for homogeneous systems. Heterogeneous infrastructures, which include IoT devices or specialized hardware, have become more widespread and require specialized tuning to optimize workload assignment, for which researchers and developers working on scheduling systems require access to heterogeneous hardware for development and testing; such data may not be available. While simulations such as CloudSim or K8sSim can provide insights, the level of detail they can offer to validate new schedulers is limited, as they are only simulations. To address this, we introduce Q8S, a tool for emulating heterogeneous Kubernetes clusters including x86_64 and ARM64 architectures on OpenStack using QEMU. Emulations created through Q8S provide a higher level of detail than simulations and can be used to train machine learning scheduling algorithms. By providing an environment capable of executing real workloads, Q8S enables researchers and developers to test and refine their scheduling algorithms, ultimately leading to more efficient and effective heterogeneous cluster management. We release our implementation of Q8S as open source. Full article

(This article belongs to the Collection Parallel and Distributed Computing: Algorithms and Applications)

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11 pages, 533 KiB

Open AccessArticle

The Clique-Width of Minimal Series-Parallel Digraphs

by Frank Gurski and Ruzayn Quaddoura

Algorithms 2025, 18(6), 323; https://doi.org/10.3390/a18060323 - 28 May 2025

MSP DAGs (short for minimal series-parallel digraphs) can be defined from the single vertex graph by applying the parallel composition and series composition. We prove an upper bound of 6 for the directed clique-width of MSP DAGs and show how a directed clique-width [...] Read more.

MSP DAGs (short for minimal series-parallel digraphs) can be defined from the single vertex graph by applying the parallel composition and series composition. We prove an upper bound of 6 for the directed clique-width of MSP DAGs and show how a directed clique-width 6-expression can be found in linear time. Our 6-expression can be used to construct an MSP DAG G from its binary decomposition tree

T (G)

in linear time. We apply our bound on the directed clique-width to conclude a number of algorithmic consequences for MSP DAGs. Full article

(This article belongs to the Special Issue Graph and Hypergraph Algorithms and Applications)

20 pages, 6673 KiB

Open AccessArticle

Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle

by Keshu Zhang, Jiandong Jia, Xiaodan Shangguan and Jing Dong

Algorithms 2025, 18(6), 322; https://doi.org/10.3390/a18060322 - 28 May 2025

Proton exchange membrane fuel cell (PEMFC) hybrid vehicles offer a long driving range but are heavily dependent on energy management strategies (EMS). Traditional EMS methods, such as rule-based approaches and optimization-based methods like model predictive control (MPC), either lack flexibility or are computationally [...] Read more.

Proton exchange membrane fuel cell (PEMFC) hybrid vehicles offer a long driving range but are heavily dependent on energy management strategies (EMS). Traditional EMS methods, such as rule-based approaches and optimization-based methods like model predictive control (MPC), either lack flexibility or are computationally complex and rely on prior driving experience. To overcome these limitations, this study proposes a semi-empirical approach that combines state machine (SM) and MPC in a novel hybrid EMS (SM-MPC) to optimize power distribution in a 100 kW PEMFC hybrid vehicle. The SM-MPC strategy uses SM to handle fast power fluctuations and MPC to manage slow variations, balancing real-time adaptability and efficiency. Simulation results based on the NEDC and HWFET driving cycles show that compared to the traditional MPC method, SM-MPC significantly reduces hydrogen consumption by 7.11 g (NEDC) and 1.89 g (HWFET). Additionally, the proposed method effectively maintains the state of charge (SOC) of the lithium-ion battery using a PID controller and ensures the PEMFC stack temperature remains within ±5.8 °C. Overall, the SM-MPC strategy improves energy efficiency, reduces fuel consumption, and enhances the stability of the hybrid power system, offering a promising solution for real-time energy optimization in fuel cell vehicles. Full article

(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)

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25 pages, 2065 KiB

Open AccessArticle

Comprehensive Risk Assessment of Power Grids Using Fuzzy Bayesian Networks Through Expert Elicitation: A Technical Analysis

by Yasir Mahmood, Nof Yasir, Nita Yodo, Ying Huang, Di Wu and Roy A. McCann

Algorithms 2025, 18(6), 321; https://doi.org/10.3390/a18060321 - 28 May 2025

Power grid infrastructures, essential to modern societies for electricity distribution, are prone to vulnerabilities due to their numerous sensitive components, necessitating a comprehensive risk assessment. Uncertainty in historical failure data often compromises accurate risk quantification, leading to the integration of expert elicitation as [...] Read more.

Power grid infrastructures, essential to modern societies for electricity distribution, are prone to vulnerabilities due to their numerous sensitive components, necessitating a comprehensive risk assessment. Uncertainty in historical failure data often compromises accurate risk quantification, leading to the integration of expert elicitation as a solution. This study develops a Bayesian network (BN) risk assessment model integrated with fuzzy set theory (FST), referred to as the fuzzy Bayesian network (FBN). By incorporating expert insights, this model quantifies internal and external risk variables more comprehensively. Crisp probabilities (CPr), derived from regional transmission operator (RTO) failure incident data, are complemented by fuzzy probabilities (FPr) from expert elicitation. The findings indicate that equipment conditions, specifically transmission lines and circuit breakers, are critical threats to power grids. Environmental factors, particularly storms, emerge as vulnerability risks. A comparison of results using both CPr plus FPr versus FPr alone underscores the utility of expert elicitation in risk assessment. This research demonstrates the effectiveness of FBNs through expert elicitation, providing a comprehensive and accurate framework for power grid risk assessment. To improve risk evaluation in critical infrastructure, integrated data collection techniques are recommended. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition)

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20 pages, 982 KiB

Open AccessArticle

Binary Decision Support Using AHP: A Model for Alternative Analysis

by Edvan Gomes da Silva, Fernando Rocha Moreira, Marcus Aurélio Carvalho Georg, Rildo Ribeiro dos Santos, Luiz Antônio Ribeiro Júnior and Rafael Rabelo Nunes

Algorithms 2025, 18(6), 320; https://doi.org/10.3390/a18060320 - 28 May 2025

Decision-making is a fundamental challenge in science and engineering, mainly when subjective factors influence the process. This paper introduces a decision support model based on the Analytic Hierarchy Process (AHP) that was specifically adapted for binary decisions and we term Binary AHP. The [...] Read more.

Decision-making is a fundamental challenge in science and engineering, mainly when subjective factors influence the process. This paper introduces a decision support model based on the Analytic Hierarchy Process (AHP) that was specifically adapted for binary decisions and we term Binary AHP. The model facilitates structured decision-making when evaluating two opposing alternatives, such as yes/no scenarios. To demonstrate its applicability, we applied the Binary AHP model to a real-world case in the Brazilian public sector, where agencies must determine whether a technological solution qualifies as an Information and Communication Technology (ICT) solution. This classification is crucial since it directly impacts procurement policies and regulatory compliance. Our results show that Binary AHP enhanced the decision consistency, transparency, and reproducibility, and reduced the subjective discrepancies between the evaluators. Additionally, by inverting the priority vectors, the model allowed for a comparative analysis of both decision alternatives, thus offering more profound insights into the classification process. This study highlights the flexibility of AHP-based decision support methodologies and proposes a structured approach to refining binary decision frameworks in complex, multi-criteria environments. Full article

(This article belongs to the Section Databases and Data Structures)

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33 pages, 12286 KiB

Open AccessArticle

A Weight Assignment-Enhanced Convolutional Neural Network (WACNN) for Freight Volume Prediction of Sea–Rail Intermodal Container Systems

by Yuhonghao Wang, Wenxin Li, Xingmin Qi and Yinzhang Yu

Algorithms 2025, 18(6), 319; https://doi.org/10.3390/a18060319 - 27 May 2025

In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on [...] Read more.

In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on a convolutional neural network (CNN) algorithm is proposed and a new feature processing method is used: weight assignment (WA). Firstly, we use qualitative methods to preliminarily select the indicators, and then use multiple interpolation to fill in the missing raw data. Next, Pearson and Spearman quantitative analysis methods are used, and the analysis results are grouped using the k-means, with the high correlation groups assigned high weights. Next, we use quadratic interpolation to obtain the daily data. Finally, a weight assignment-enhanced convolutional neural network (WACNN) model and seven other mainstream models are constructed, using the Yingkou port container throughput prediction as a case study. The research results indicate that the WACNN prediction model has the best performance and strong robustness. The research results can provide a reference basis for the planning of sea–rail intermodal container transportation and the allocation of transportation resources, and achieve the overall efficiency improvement of logistics systems. Full article

(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))

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48 pages, 1559 KiB

Open AccessReview

A Review of Key Signal Processing Techniques for Structural Health Monitoring: Highlighting Non-Parametric Time-Frequency Analysis, Adaptive Decomposition, and Deconvolution

by Yixin Zhou, Zepeng Ma and Lei Fu

Algorithms 2025, 18(6), 318; https://doi.org/10.3390/a18060318 - 27 May 2025

This paper reviews key signal processing techniques in structural health monitoring (SHM), focusing on non-parametric time–frequency analysis, adaptive decomposition, and deconvolution methods. It examines the short-time Fourier transform (STFT), wavelet transform (WT), and Wigner–Ville distribution (WVD), highlighting their applications, advantages, and limitations in [...] Read more.

This paper reviews key signal processing techniques in structural health monitoring (SHM), focusing on non-parametric time–frequency analysis, adaptive decomposition, and deconvolution methods. It examines the short-time Fourier transform (STFT), wavelet transform (WT), and Wigner–Ville distribution (WVD), highlighting their applications, advantages, and limitations in SHM. The review also explores adaptive techniques like empirical mode decomposition (EMD) and its variants (EEMD, MEEMD), as well as variational mode decomposition (VMD) and its improved versions (SVMD, AVMD), emphasizing their effectiveness in handling nonlinear and non-stationary signals. Additionally, deconvolution methods such as minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) are discussed for mechanical fault diagnosis. The paper aims to provide a comprehensive overview of these techniques, offering insights for future research into SHM signal processing. Full article

(This article belongs to the Special Issue Algorithms and Applications of Machine Learning Techniques for Healthcare)

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18 pages, 1069 KiB

Open AccessArticle

AI for Data Quality Auditing: Detecting Mislabeled Work Zone Crashes Using Large Language Models

by Shadi Jaradat, Nirmal Acharya, Smitha Shivshankar, Taqwa I. Alhadidi and Mohammad Elhenawy

Algorithms 2025, 18(6), 317; https://doi.org/10.3390/a18060317 - 27 May 2025

Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. This study presents an AI-assisted framework for auditing crash data integrity by detecting potentially mislabeled records related to construction zone (czone) involvement. A GPT-3.5 model was fine-tuned [...] Read more.

Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. This study presents an AI-assisted framework for auditing crash data integrity by detecting potentially mislabeled records related to construction zone (czone) involvement. A GPT-3.5 model was fine-tuned using a fusion of structured crash attributes and unstructured narrative text (i.e., multimodal input) to predict work zone involvement. The model was applied to 6400 crash reports to flag discrepancies between predicted and recorded labels. Among 80 flagged mismatches, expert review confirmed four records as genuine misclassifications, demonstrating the framework’s capacity to surface high-confidence labeling errors. The model achieved strong overall accuracy (98.75%) and precision (86.67%) for the minority class, but showed low recall (14.29%), reflecting its conservative design that minimizes false positives in an imbalanced dataset. This precision-focused approach supports its use as a semi-automated auditing tool, capable of narrowing the scope for expert review and improving the reliability of large-scale traffic safety datasets. The framework is also adaptable to other misclassified crash attributes or domains where structured and unstructured data can be fused for data quality assurance. Full article

(This article belongs to the Section Databases and Data Structures)

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28 pages, 2243 KiB

Open AccessArticle

Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank

by Xinru Xue, Ting Shu and Jinsong Xia

Algorithms 2025, 18(6), 316; https://doi.org/10.3390/a18060316 - 27 May 2025

Metaheuristic algorithms, due to their superior global exploration capabilities and applicability, have emerged as critical tools for addressing complicated optimization tasks. However, these algorithms commonly depend on expert knowledge to configure parameters and design strategies. As a result, they frequently lack appropriate automatic [...] Read more.

Metaheuristic algorithms, due to their superior global exploration capabilities and applicability, have emerged as critical tools for addressing complicated optimization tasks. However, these algorithms commonly depend on expert knowledge to configure parameters and design strategies. As a result, they frequently lack appropriate automatic behavior adjustment methods for dealing with changing problem features or dynamic search phases, limiting their adaptability, search efficiency, and solution quality. To address these limitations, this paper proposes an automated hybrid metaheuristic algorithm generation method based on Learning to Rank (LTR-MHA). The LTR-MHA aims to achieve adaptive optimization of algorithm combination strategies by dynamically fusing the search behaviors of Whale Optimization (WOA), Harris Hawks Optimization (HHO), and the Genetic Algorithm (GA). At the core of the LTR-MHA is the utilization of Learning-to-Rank techniques to model the mapping between problem features and algorithmic behaviors, to assess the potential of candidate solutions in real-time, and to guide the algorithm to make better decisions in the search process, thereby achieving a well-adjusted balance between the exploration and exploitation stages. The effectiveness and efficiency of the LTR-MHA method are evaluated using the CEC2017 benchmark functions. The experiments confirm the effectiveness of the proposed method. It delivers superior results compared to individual metaheuristic algorithms and random combinatorial strategies. Notable improvements are seen in average fitness, solution precision, and overall stability. Our approach offers a promising direction for efficient search capabilities and adaptive mechanisms in automated algorithm design. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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8 pages, 252 KiB

Open AccessArticle

General Position Subset Selection in Line Arrangements

by Adrian Dumitrescu

Algorithms 2025, 18(6), 315; https://doi.org/10.3390/a18060315 - 27 May 2025

Given a set of n points in a plane, the General Position Subset Selection problem is that of finding a maximum-size subset of points in general position, i.e., with no three points collinear. The problem is known to be hard computationally, and the [...] Read more.

Given a set of n points in a plane, the General Position Subset Selection problem is that of finding a maximum-size subset of points in general position, i.e., with no three points collinear. The problem is known to be hard computationally, and the best approximation ratio known is

Ω (n^{- 1 / 2})

. Here, we obtain better approximations in three special cases: (I) a constant-factor approximation for the case where the input set consists of lattice points and is dense, which means that the ratio between the maximum and the minimum distance in P is of the order of

Θ (\sqrt{n})

; (II) an

Ω ({(log n)}^{- 1 / 2})

-approximation for the case where the input set is the set of vertices of a genericn-line arrangement, i.e., one with

Ω (n^{2})

vertices; and (III) an

Ω ({(log n)}^{- 1 / 2})

-approximation for the case where the input set has at most

O (\sqrt{n})

points collinear and can be covered by

O (\sqrt{n})

lines. The scenario in (I) is a special case of that in (II). Our approximations rely on probabilistic methods and results from incidence geometry. Full article

(This article belongs to the Collection Feature Papers in Randomized, Online and Approximation Algorithms)

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22 pages, 780 KiB

Open AccessReview

Multi-Project Scheduling with Uncertainty and Resource Flexibility: A Narrative Review and Exploration of Future Landscapes

by Marzieh Aghileh, Anabela Tereso, Filipe Alvelos and Maria Odete Monteiro Lopes

Algorithms 2025, 18(6), 314; https://doi.org/10.3390/a18060314 - 26 May 2025

This paper presents a narrative review on the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) under uncertainty and resource flexibility. Traditional project scheduling assumes complete information and a deterministic environment where a pre-computed baseline schedule is executed. However, real-world projects frequently face uncertainty, such as [...] Read more.

This paper presents a narrative review on the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) under uncertainty and resource flexibility. Traditional project scheduling assumes complete information and a deterministic environment where a pre-computed baseline schedule is executed. However, real-world projects frequently face uncertainty, such as variable task durations and fluctuating resource availability. Analyzing studies from 2013 to 2024, this review examines optimization models addressing multiple objectives, including minimizing project duration, cost, and resource leveling. It categorizes solution approaches, from exact algorithms to heuristic and metaheuristic methods, while reviewing the primary instance sets and benchmarks used in the field. Additionally, it highlights the value of flexible resource management approaches that enable adaptive responses to real-time project demands, thereby enhancing scheduling robustness. By systematically addressing RCMPSP under uncertainty, this paper provides a valuable framework for researchers and practitioners seeking to develop resilient, adaptive scheduling solutions for complex, dynamic project environments. Full article

(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)

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17 pages, 2389 KiB

Open AccessArticle

Improved Asynchronous Federated Learning for Data Injection Pollution

by Aiyou Li, Huoyou Li, Yanfang Liu and Guoli Ji

Algorithms 2025, 18(6), 313; https://doi.org/10.3390/a18060313 - 26 May 2025

In view of the problems of data pollution, incomplete feature extraction, and poor multi-network parameter sharing and transmission under the federated learning framework of deep learning, this article proposes an improved asynchronous federated learning algorithm of multi-model fusion based on data injection pollution. [...] Read more.

In view of the problems of data pollution, incomplete feature extraction, and poor multi-network parameter sharing and transmission under the federated learning framework of deep learning, this article proposes an improved asynchronous federated learning algorithm of multi-model fusion based on data injection pollution. Through data augmentation, the existing dataset is preprocessed to enhance the algorithm’s ability to identify the noise data. In our approach, the residual network is used to extract the static information of the image, the capsule network is used to extract the spatial dependence among the internal structures of the image, several layers of convolution are used to reduce the dimensions of both features, and the two extracted features are fused. In order to reduce the transmission overhead of parameters shared between the residual network and capsule network, we adopt an asynchronous parameter transmission between the global trainer and the local trainer. When the global trainer broadcasts the parameters to each local trainer, several trainers are randomly selected to avoid communication link blockage. Finally, through conducting various experiments, the results show that our alogrithm can effectively extract the pathological features in the image and achieve higher accuracy, outperforming the current mainstream algorithms. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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27 pages, 3572 KiB

Open AccessArticle

Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm

by Ilie Cirstea, Andrei-Flavius Radu, Delia Mirela Tit, Ada Radu, Gabriela Bungau and Paul Andrei Negru

Algorithms 2025, 18(6), 312; https://doi.org/10.3390/a18060312 - 26 May 2025

The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The rising volume of [...] Read more.

The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The rising volume of MW, exacerbated by global health crises, strains existing systems. This study uses bibliometric analysis of 3025 publications from 1975 to 2024, employing Web of Science data with specific Boolean operators and keywords for efficient searching algorithms. Data visualization and analysis were carried out with software such as VOSviewer version 1.6.20 and Bibliometrix 5.0.0, along with custom Python 3.12.3 thesaurus files to standardize terminology. The results reveal a significant rise in publications post-2000, particularly during the COVID-19 pandemic, with China, India, and the US as major contributors. South Korea stands out for high citation rates. Network analysis identified collaboration patterns, while trend mapping highlighted a shift toward sustainable waste management practices. The evaluation insights revealed a clear transition from incineration-based methods toward sustainable and innovative solutions such as autoclaving, plasma pyrolysis, and advanced oxidation processes, driven by environmental concerns and regulatory frameworks. This study underscores the implications of MW and the importance of analyzing publication trends over time to understand the ongoing need for development, grounded in a legislative policy framework, which is essential for advancing sustainable practices in MW management. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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22 pages, 3259 KiB

Open AccessArticle

DataToCare: Predicting Treatments for Intensive Care Unit Patients Based on Similarity of Abnormalities

by Shan Randhawa, Abbas Shojaee, Elisa Sorrentino, Yifan Li, Azza Abouzied and Dennis Shasha

Algorithms 2025, 18(6), 311; https://doi.org/10.3390/a18060311 - 26 May 2025

Clinical decision-making lies at the heart of health care. Medical data collection has made it possible for clinical decision-making to be data-driven. However, data-driven systems for decision-making have so far worked only for a limited set of clinical conditions. It is still unclear [...] Read more.

Clinical decision-making lies at the heart of health care. Medical data collection has made it possible for clinical decision-making to be data-driven. However, data-driven systems for decision-making have so far worked only for a limited set of clinical conditions. It is still unclear whether a pure data-driven clinical decision-making system can work for a wide set of clinical conditions in real-time environments such as Intensive Care Units. Our DataToCare system receives demographic information, initial diagnoses and measurements from the first hours of a patient’s arrival in an Intensive Care Unit. From that information, DataToCare suggests treatments to offer the patient, based on the treatments given to similar patients. Patients are considered similar if they have abnormal measurements in common. This paper describes the analytics pipeline and the results of its evaluation. DataToCare has the potential to increase patient safety and transfer expertise across medical teams. Though we apply these ideas in the context of Intensive Care Units, the approach could potentially be applied more broadly within medicine. Full article

(This article belongs to the Collection Feature Papers in Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems)

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24 pages, 2716 KiB

Open AccessArticle

Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability

by Maxim Zhabinets, Benjamin Tyler, Martin Lukac, Shinobu Nagayama, Ferdinand Molnár and Michitaka Kameyama

Algorithms 2025, 18(6), 310; https://doi.org/10.3390/a18060310 - 25 May 2025

The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios [...] Read more.

The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios with limited data availability. This paper addresses a critical challenge: where additional data might not be available for training an Algorithm selector, and to implement a selection mechanism, data must be generated. Focusing on medical image classification, we investigate whether synthetic data can effectively train an Algorithm selector when real training data are scarce. Our methodology involves data generation using Generative Adversarial Network. To determine if Algorithm selection trained on synthetically generated data can achieve the same accuracy as if trained on real-world natural data, we systematically evaluate the data generative model using the smallest amount of data needed to choose the right Algorithm and to achieve the expected level of accuracy. Our experimental results demonstrate that using a small amount of real samples can provide enough information to a Generative Adversarial Network to synthesize a new dataset that, when used for training the Algorithm selection, improves image classification in some cases. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Image Processing)

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18 pages, 1414 KiB

Open AccessArticle

Combining the A* Algorithm with Neural Networks to Solve the Team Orienteering Problem with Obstacles and Environmental Factors

by Alfons Freixes, Javier Panadero, Angel A. Juan and Carles Serrat

Algorithms 2025, 18(6), 309; https://doi.org/10.3390/a18060309 - 25 May 2025

This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To [...] Read more.

This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To achieve this, we employ a simheuristic algorithm for the overall route optimization, while integrating the A* algorithm to determine feasible paths between nodes that avoid obstacles in a 2D grid-based environment. Then, a feedforward neural network estimates travel time based on UAV speed, wind conditions, trajectory distance, and payload weight. This estimation is incorporated into the optimization process to improve route planning accuracy. Numerical experiments evaluate the impact of various parameters, including obstacle placement, UAV speed, wind conditions, and payload weight. These experiments include maps with 30 to 100 points of interest and varying obstacle densities and show that our hybrid method improves solution quality by up to

15 %

in total profit compared to a baseline approach. Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency. Full article

(This article belongs to the Special Issue Intelligent Resource Allocation for Unmanned Aerial Vehicle-Assisted Wireless Networks)

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22 pages, 8623 KiB

Open AccessArticle

Polarization in Political Rallies: A Markovian Agent-Based Model for Opinion Dynamics

by Marco Scarpa, Marco Garofalo, Francesco Longo and Salvatore Serrano

Algorithms 2025, 18(6), 308; https://doi.org/10.3390/a18060308 - 23 May 2025

One of the most studied problems among researchers in recent years is how individuals form their opinions. This problem has become more urgent with the advent of social networks, which can easily influence a huge number of followers and have become increasingly pervasive [...] Read more.

One of the most studied problems among researchers in recent years is how individuals form their opinions. This problem has become more urgent with the advent of social networks, which can easily influence a huge number of followers and have become increasingly pervasive over time. The produced effect is the rise of polarized opinions among different groups of people. Understanding polarization is of great relevance across various application domains, such as economics and politics. Opinion dynamics has often been studied by exploiting the popular Friedkin–Johnsen model. In this paper, we propose a different modeling approach based on the Markovian agents paradigm for deriving metrics characterizing polarized opinions. The main goal of this work is to demonstrate the potential of the Markovian agent modeling paradigm for the analysis of opinion dynamics. The main advantages of Markovian agents are the ease of setting a large number of behavioral parameters, spatial distribution of agents, scalability, and numerical tractability. We extend our previous work, in which we analyzed a peer assembly and validated it against other commonly used modeling approaches. In our opinion, the Markovian agent approach offers an effective modeling framework due to its scalability and flexibility in handling parameters that describe the behavior of individuals in the opinion formation process. The context we will discuss is inspired by election rallies, where an assembly attends a speech by a political candidate. The crowd consists of individuals with diverse initial political opinions, and the candidate seeks to polarize them toward his/her own political stance. Full article

(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (3rd Edition))

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