Algorithms

22 pages, 4258 KB

Open AccessArticle

Visible Image-Based Machine Learning for Identifying Abiotic Stress in Sugar Beet Crops

by Seyed Reza Haddadi, Masoumeh Hashemi, Richard C. Peralta and Masoud Soltani

Algorithms 2025, 18(11), 680; https://doi.org/10.3390/a18110680 (registering DOI) - 24 Oct 2025

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused [...] Read more.

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as inputs to a Multilayer Perceptron, achieving 96.67% accuracy for overall stress detection, and 95.93% and 94.44% for water and nitrogen stress identification, respectively. A Random Forest model, using only the green band, achieved 92.22% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using a Scale-Invariant Feature Transform descriptor (SIFT) achieved 93.33% for water stress, while RF with RGB bands and canopy cover reached 85.56% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency. Full article

(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)

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29 pages, 2616 KB

Open AccessArticle

Adaptive Real-Time Planning of Trailer Assignments in High-Throughput Cross-Docking Terminals

by Tamás Bányai and Sebastian Trojahn

Algorithms 2025, 18(11), 679; https://doi.org/10.3390/a18110679 - 24 Oct 2025

Abstract

Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We [...] Read more.

Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We propose a practical framework that helps logistics terminals assign trailers to docks in real time. It links live sensor data with a mathematical optimization model, so that the system can quickly adjust trailer plans when traffic or workload changes. Real-time data from IoT sensors, GPS, and operational records are preprocessed, enriched with predictive analytics, and used as input for a Mixed-Integer Linear Programming (MILP) model solved in rolling horizons. This enables the continuous reallocation of inbound and outbound trailers, ensuring synchronized flows and balanced dock utilization. Numerical experiments compare the adaptive approach with conventional first-come-first-served scheduling. Results show that average inbound dock utilization improves from 68% to 71%, while the share of periods with full utilization increases from 33.3% to 41.4%. Outbound utilization also rises from 57% to 62%. Moreover, trailer delays are significantly reduced, and the overall makespan shortens from 45 to 40 time slots. These findings confirm that adaptive, real-time trailer assignment can enhance efficiency, reliability, and resilience in cross-docking operations. The proposed framework thus bridges the gap between static optimization models and the operational requirements of modern, high-throughput logistics hubs. Full article

(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (4th Edition))

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14 pages, 1596 KB

Open AccessArticle

A Hybrid Quantum–Classical Spectral Solver for Nonlinear Differential Equations

by Samar A. Aseeri

Algorithms 2025, 18(11), 678; https://doi.org/10.3390/a18110678 - 23 Oct 2025

Abstract

We investigate hybrid quantum–classical solvers for nonlinear boundary value problems using Chebyshev spectral collocation. Unlike prior methods such as H–DES, which repeatedly recompile circuits and encode the entire spectral basis on the quantum processor, our framework offloads only the residual minimisation to a [...] Read more.

We investigate hybrid quantum–classical solvers for nonlinear boundary value problems using Chebyshev spectral collocation. Unlike prior methods such as H–DES, which repeatedly recompile circuits and encode the entire spectral basis on the quantum processor, our framework offloads only the residual minimisation to a quantum backend while retaining classical enforcement of boundary conditions. Two paradigms are considered: (i) gate-based residual minimisation on CUDA-Q using variational circuits to evaluate a Cubic Unconstrained Binary Optimisation (CUBO) cost, which naturally arises from the discretisation, and (ii) a Quadratic Unconstrained Binary Optimisation (QUBO) reformulation, which is required for execution on a quantum annealer, executed via a classical–quantum mapping. We further explore a CUBO extension on CUDA-Q and direct residual-to-energy mapping on annealers. Benchmarks confirm that the classical solver reproduces the analytic solution with spectral accuracy; among quantum-enhanced methods, the annealer-based QUBO yields the closest approximation. The gate-based CUBO solver improves upon a legacy variational baseline but exhibits a small interior bias due to limited circuit depth and precision. These findings underscore the complementary roles of annealers and gate-based devices in hybrid scientific computing and demonstrate a feasible workflow for the NISQ era rather than a speedup over classical methods. Recent progress in quantum algorithms for differential equations signals a rapidly maturing field with significant potential for practical quantum advantage. Full article

(This article belongs to the Special Issue Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing)

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26 pages, 4757 KB

Open AccessArticle

MORA: A Multicriteria Optimal Resource Allocation and Decision Support Toolkit for Wildfire Management

by Theofanis Orphanoudakis, Christos Betzelos and Helen Catherine Leligou

Algorithms 2025, 18(11), 677; https://doi.org/10.3390/a18110677 - 23 Oct 2025

Abstract

Forest ecosystems are vital to sustainable development, contributing to economic, environmental and social well-being. However, the increasing frequency and severity of wildfires threaten these ecosystems, demanding more effective and integrated fire management (IFM) strategies. Current suppression efforts face limitations due to high resource [...] Read more.

Forest ecosystems are vital to sustainable development, contributing to economic, environmental and social well-being. However, the increasing frequency and severity of wildfires threaten these ecosystems, demanding more effective and integrated fire management (IFM) strategies. Current suppression efforts face limitations due to high resource demands and the need for timely, informed decision-making under uncertain conditions. This paper presents the SILVANUS project’s approach to developing an advanced Decision Support System (DSS) designed to assist incident commanders in optimizing resource allocation during wildfire events. Leveraging Geographic Information Systems (GIS), real-time data collection, AI-enhanced analytics and multicriteria optimization algorithms, the SILVANUS DSS component integrates diverse data sources to support dynamic, risk-informed decisions. The system operates within a cloud-edge infrastructure to ensure scalability, interoperability and secure data management. We detail the formalization of the resource allocation problem, describe the implementation of the DSS within the SILVANUS platform, and evaluate its performance in both controlled simulations and real-world pilot scenarios. The results demonstrate the system’s potential to enhance situational awareness and improve the effectiveness of wildfire response operations. Full article

(This article belongs to the Special Issue Multi-Objective and Multi-Level Optimization: Algorithms and Applications (2nd Edition))

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16 pages, 813 KB

Open AccessReview

A Review of Urban Path Planning Algorithms in Intelligent Transportation Systems

by Zhenyu Tian, Huaqi Yao and Yu Shao

Algorithms 2025, 18(11), 676; https://doi.org/10.3390/a18110676 - 23 Oct 2025

Abstract

With the accelerating pace of urbanization and the increasing complexity of traffic systems, urban transportation faces growing challenges such as congestion, inefficiency, and environmental strain. Path planning algorithms—key components in intelligent transportation systems—have evolved from classical graph-based methods like Dijkstra and A* to [...] Read more.

With the accelerating pace of urbanization and the increasing complexity of traffic systems, urban transportation faces growing challenges such as congestion, inefficiency, and environmental strain. Path planning algorithms—key components in intelligent transportation systems—have evolved from classical graph-based methods like Dijkstra and A* to modern approaches leveraging metaheuristics and deep learning. This paper systematically reviews the development of urban path planning algorithms, tracing their progression from foundational methods to state-of-the-art techniques such as Ant Colony Optimization, Probabilistic Roadmaps, and Rapidly Exploring Random Trees. Recent innovations, including improved genetic algorithms, hybrid A* variants, and reinforcement learning models, are analyzed in terms of adaptability, efficiency, and real-time performance. Furthermore, the review highlights ongoing challenges in scalability, dynamic adaptation, and algorithmic fairness, while discussing future directions that integrate technical innovation with policy and ethical considerations to support sustainable and equitable urban mobility. Full article

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25 pages, 720 KB

Open AccessArticle

Variational Bayesian Inference for a Q-Matrix-Free Hidden Markov Log-Linear Additive Cognitive Diagnostic Model

by Hao Duan, James Tang, Matthew J. Madison, Michael Cotterell and Minjeong Jeon

Algorithms 2025, 18(11), 675; https://doi.org/10.3390/a18110675 - 22 Oct 2025

Abstract

Cognitive diagnostic models (CDMs) are commonly used in educational assessment to uncover the specific cognitive skills that contribute to student performance, allowing for precise identification of individual strengths and weaknesses and the design of targeted interventions. Traditional CDMs, however, depend heavily on a [...] Read more.

Cognitive diagnostic models (CDMs) are commonly used in educational assessment to uncover the specific cognitive skills that contribute to student performance, allowing for precise identification of individual strengths and weaknesses and the design of targeted interventions. Traditional CDMs, however, depend heavily on a predefined Q-matrix that specifies the relationship between test items and underlying attributes. In this study, we introduce a hidden Markov log-linear additive cognitive diagnostic model (HM-LACDM) that does not require a Q-matrix, making it suitable for analyzing longitudinal assessment data without prior structural assumptions. To support scalable applications, we develop a variational Bayesian inference (VI) algorithm that enables efficient estimation in large datasets. Additionally, we propose a method to reconstruct the Q-matrix from estimated item-effect parameters. The effectiveness of the proposed approach is demonstrated through simulation studies. Full article

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

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26 pages, 1737 KB

Open AccessArticle

ECG-CBA: An End-to-End Deep Learning Model for ECG Anomaly Detection Using CNN, Bi-LSTM, and Attention Mechanism

by Khalid Ammar, Salam Fraihat, Ghazi Al-Naymat and Yousef Sanjalawe

Algorithms 2025, 18(11), 674; https://doi.org/10.3390/a18110674 - 22 Oct 2025

Abstract

The electrocardiogram (ECG) is a vital diagnostic tool used to monitor heart activity and detect cardiac abnormalities, such as arrhythmias. Accurate classification of normal and abnormal heartbeats is essential for effective diagnosis and treatment. Traditional deep learning methods for automated ECG classification primarily [...] Read more.

The electrocardiogram (ECG) is a vital diagnostic tool used to monitor heart activity and detect cardiac abnormalities, such as arrhythmias. Accurate classification of normal and abnormal heartbeats is essential for effective diagnosis and treatment. Traditional deep learning methods for automated ECG classification primarily focus on reconstructing the original ECG signal and detecting anomalies based on reconstruction errors, which represent abnormal features. However, these approaches struggle with unseen or underrepresented abnormalities in the training data. In addition, other methods rely on manual feature extraction, which can introduce bias and limit their adaptability to new datasets. To overcome this problem, this study proposes an end-to-end model called ECG-CBA, which integrates the convolutional neural networks (CNNs), bidirectional long short-term memory networks (Bi-LSTM), and a multi-head Attention mechanism. ECG-CBA model learns discriminative features directly from the original dataset rather than relying on feature extraction or signal reconstruction. This enables higher accuracy and reliability in detecting and classifying anomalies. The CNN extracts local spatial features from raw ECG signals, while the Bi-LSTM captures the temporal dependencies in sequential data. An attention mechanism enables the model to primarily focus on critical segments of the ECG, thereby improving classification performance. The proposed model is trained on normal and abnormal ECG signals for binary classification. The ECG-CBA model demonstrates strong performance on the ECG5000 and MIT-BIH datasets, achieving accuracies of 99.60% and 98.80%, respectively. The model surpasses traditional methods across key metrics, including sensitivity, specificity, and overall classification accuracy. This offers a robust and interpretable solution for both ECG-based anomaly detection and cardiac abnormality classification. Full article

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14 pages, 1459 KB

Open AccessArticle

Algorithms for Two Types of Topological Indices

by Fengqin Deng and Tingzeng Wu

Algorithms 2025, 18(11), 673; https://doi.org/10.3390/a18110673 - 22 Oct 2025

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Abstract

Topological indices are closely related to the stability and physical properties (such as the boiling point) of chemical molecules. The permanental sum and Hosoya index are two topological indices that are strongly associated with molecular structure. In this paper, we present algorithms for [...] Read more.

Topological indices are closely related to the stability and physical properties (such as the boiling point) of chemical molecules. The permanental sum and Hosoya index are two topological indices that are strongly associated with molecular structure. In this paper, we present algorithms for the permanental sum and Hosoya index. As an application, we employ these two algorithms to calculate the permanental sum and Hosoya index of (3,6)-fullerene with

n (4 \leq n \leq 44)

vertices. These results provide a mathematical reference for the synthesis of small (3,6)-fullerenes. Full article

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25 pages, 9213 KB

Open AccessArticle

Q-Learning-Based Multi-Strategy Topology Particle Swarm Optimization Algorithm

by Xiaoxi Hao, Shenwei Wang, Xiaotong Liu, Tianlei Wang, Guangfan Qiu and Zhiqiang Zeng

Algorithms 2025, 18(11), 672; https://doi.org/10.3390/a18110672 - 22 Oct 2025

Viewed by 55

Abstract

In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, [...] Read more.

In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, where Q-learning dynamically selects among fully informed topology, small-world topology, and exemplar-set topology to achieve an adaptive balance between global exploration and local exploitation. Furthermore, the algorithm integrates differential evolution perturbations and a global optimal restart strategy based on stagnation detection, together with a dual-layer experience replay mechanism to enhance population diversity at multiple levels and strengthen the ability to escape local optima. Experimental results on 29 CEC2017 benchmark functions, compared against various PSO variants and other advanced evolutionary algorithms, show that MSTPSO achieves superior fitness performance and exhibits stronger stability on high-dimensional and complex functions. Ablation studies further validate the critical contribution of the Q-learning-based multi-topology control and stagnation detection mechanisms to performance improvement. Overall, MSTPSO demonstrates significant advantages in convergence accuracy and global search capability. Full article

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18 pages, 851 KB

Open AccessArticle

Learning System-Optimal and Individual-Optimal Collision Avoidance Behaviors by Autonomous Mobile Agents

by Katsutoshi Hirayama, Kazuma Gohara, Jinichi Koue, Tenda Okimoto and Donggyun Kim

Algorithms 2025, 18(11), 671; https://doi.org/10.3390/a18110671 - 22 Oct 2025

Viewed by 66

Abstract

Automated collision avoidance is a central topic in multi-agent systems that consist of mobile agents. One simple approach to pursue system-wide performance is a centralized algorithm, which, however, becomes computationally expensive when involving a large number of agents. There have thus been proposed [...] Read more.

Automated collision avoidance is a central topic in multi-agent systems that consist of mobile agents. One simple approach to pursue system-wide performance is a centralized algorithm, which, however, becomes computationally expensive when involving a large number of agents. There have thus been proposed fully distributed collision avoidance algorithms that can naturally handle many-to-many encounter situations. The DSSA⁺ is one of those algorithms, which is heuristic and incomplete but has lower communication and computation overheads than other counterparts. However, the DSSA⁺ and some other distributed collision avoidance algorithms basically optimize the agents’ behavior only in the short term, not caring about the total efficiency in their paths. This may result in some agents’ paths with over-deviation or over-stagnation. In this paper, we present Distributed Stochastic Search algorithm with a deep Q-network (DSSQ), in which the agents can generate time-efficient collision-free paths while they learn independently whether to detour or change speeds by Deep Reinforcement Learning. A key idea in the learning principle of the DSSQ is to let the agents pursue their individual optimality. We have experimentally confirmed that a sequence of short-term system-optimal solutions found by the DSSA⁺ gradually becomes long-term individually optimal for every agent. Full article

(This article belongs to the Special Issue Multi-Objective and Multi-Level Optimization: Algorithms and Applications (2nd Edition))

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Algorithms, Volume 18, Issue 11 (November 2025) – 10 articles

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