Algorithms

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

Abstract

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, 8613 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

Abstract

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))

17 pages, 5002 KiB

Open AccessArticle

Structure Approximation-Based Preconditioning for Solving Tempered Fractional Diffusion Equations

by Xuan Zhang and Chaojie Wang

Algorithms 2025, 18(6), 307; https://doi.org/10.3390/a18060307 - 23 May 2025

Abstract

Tempered fractional diffusion equations constitute a critical class of partial differential equations with broad applications across multiple physical domains. In this paper, the Crank–Nicolson method and the tempered weighted and shifted Grünwald formula are used to discretize the tempered fractional diffusion equations. The [...] Read more.

Tempered fractional diffusion equations constitute a critical class of partial differential equations with broad applications across multiple physical domains. In this paper, the Crank–Nicolson method and the tempered weighted and shifted Grünwald formula are used to discretize the tempered fractional diffusion equations. The discretized system has the structure of the sum of the identity matrix and a diagonal matrix multiplied by a symmetric positive definite (SPD) Toeplitz matrix. For the discretized system, we propose a structure approximation-based preconditioning method. The structure approximation lies in two aspects: the inverse approximation based on the row-by-row strategy and the SPD Toeplitz approximation by the

τ

matrix. The proposed preconditioning method can be efficiently implemented using the discrete sine transform (DST). In spectral analysis, it is found that the eigenvalues of the preconditioned coefficient matrix are clustered around 1, ensuring fast convergence of Krylov subspace methods with the new preconditioner. Numerical experiments demonstrate the effectiveness of the proposed preconditioner. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 3rd Edition)

38 pages, 1287 KiB

Open AccessArticle

Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs

by Gedeon Hakizimana and Agapito Ledezma Espino

Algorithms 2025, 18(6), 306; https://doi.org/10.3390/a18060306 - 23 May 2025

Abstract

The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language models—have enhanced output readability, they often lack traceable and verifiable [...] Read more.

The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language models—have enhanced output readability, they often lack traceable and verifiable reasoning that is aligned with domain-specific logic. This paper presents Nomological Deductive Reasoning (NDR), supported by Nomological Deductive Knowledge Representation (NDKR), as a framework aimed at improving the transparency and auditability of AI decisions through the integration of formal logic and structured domain knowledge. NDR enables the generation of causal, rule-based explanations by validating statistical predictions against symbolic domain constraints. The framework is evaluated on a credit-risk classification task using the Statlog (German Credit Data) dataset, demonstrating that NDR can produce coherent and interpretable explanations consistent with expert-defined logic. While primarily focused on technical integration and deductive validation, the approach lays a foundation for more transparent and norm-compliant AI systems. This work contributes to the growing formalization of XAI by aligning statistical inference with symbolic reasoning, offering a pathway toward more interpretable and verifiable AI decision-making processes. Full article

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

36 pages, 1243 KiB

Open AccessArticle

A Semi-Supervised-Learning-Aided Explainable Belief Rule-Based Approach to Predict the Energy Consumption of Buildings

by Sami Kabir, Mohammad Shahadat Hossain and Karl Andersson

Algorithms 2025, 18(6), 305; https://doi.org/10.3390/a18060305 (registering DOI) - 23 May 2025

Abstract

Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the limited availability of supervised labeled data to train Artificial [...] Read more.

Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the limited availability of supervised labeled data to train Artificial Intelligence (AI) models. This data availability becomes either expensive or difficult due to privacy protection. To overcome the scarcity of balanced labeled data, semi-supervised learning utilizes extensive unlabeled data. Motivated by this, we propose semi-supervised learning to train AI model. For the AI model, we employ the Belief Rule-Based Expert System (BRBES) because of its domain knowledge-based prediction and uncertainty handling mechanism. For improved accuracy of the BRBES, we utilize initial labeled data to optimize BRBES’ parameters and structure through evolutionary learning until its accuracy reaches the confidence threshold. As semi-supervised learning, we employ a self-training model to assign pseudo-labels, predicted by the BRBES, to unlabeled data generated through weak and strong augmentation. We reoptimize the BRBES with labeled and pseudo-labeled data, resulting in a semi-supervised BRBES. Finally, this semi-supervised BRBES explains its prediction to the end-user in nontechnical human language, resulting in a trust relationship. To validate our proposed semi-supervised explainable BRBES framework, a case study based on Skellefteå, Sweden, is used to predict and explain energy consumption of buildings. Experimental results show 20 ± 0.71% higher accuracy of the semi-supervised BRBES than state-of-the-art semi-supervised machine learning models. Moreover, the semi-supervised BRBES framework turns out to be 29 ± 0.67% more explainable than these semi-supervised machine learning models. Full article

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

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18 pages, 4951 KiB

Open AccessArticle

Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN–GCE Model

by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Jiaming Chen and Xuelian Yu

Algorithms 2025, 18(6), 304; https://doi.org/10.3390/a18060304 - 23 May 2025

Abstract

Noises on the field can affect the electromechanical system characteristics in the bearing fault diagnostic process. This paper presents a deep learning-based fault-diagnosis model DRSN–GCE (Deep Relative Shrinkage Network with Gated Convolutions and Enhancements), which is designed to deal with noise and improve [...] Read more.

Noises on the field can affect the electromechanical system characteristics in the bearing fault diagnostic process. This paper presents a deep learning-based fault-diagnosis model DRSN–GCE (Deep Relative Shrinkage Network with Gated Convolutions and Enhancements), which is designed to deal with noise and improve noise resistance. In the first step, the data are preprocessed by adding different noises with different ratios of signal to noise and different frequencies to the vibration signals. This simulates the field noise environments. The continuous wavelet transformation (CWT), which converts the time-series signal from one dimension to a time-frequency two-dimensional image, provides rich data input for the deep learning model. Secondly, a convolutional gated layer is added to the deep residual network (DRSN), which suppresses the noise interference. The residual connection structure has also been improved in order to improve the transfer of features. In complex signals, the Gated Convolutional Shrinkage Module is used to improve feature extraction and suppress noise. The experiments on the Case Western Reserve University bearing dataset show that the DRSN–GCE exhibits high diagnostic accuracy and strong noise immunity in various noise environments such as Gauss, Laplace, Salt-and-Pepper, and Poisson. DRSN–GCE is superior to other deep learning models in terms of noise suppression, fault detection accuracy, and rolling bearing fault diagnoses in noisy environments. Full article

(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis)

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25 pages, 4163 KiB

Open AccessArticle

Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application

by Chisom Onyenagubo, Yasser Ismail, Radian Belu and Fred Lacy

Algorithms 2025, 18(6), 303; https://doi.org/10.3390/a18060303 - 23 May 2025

Abstract

Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) [...] Read more.

Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts. Full article

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

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16 pages, 2889 KiB

Open AccessArticle

Enhanced Viral Genome Classification Using Large Language Models

by Hemalatha Gunasekaran, Nesaian Reginal Wilfred Blessing, Umar Sathic and Mohammad Shahid Husain

Algorithms 2025, 18(6), 302; https://doi.org/10.3390/a18060302 - 22 May 2025

Abstract

The classification of genomic sequences is a crucial area of research in the field of virology. This is due to the increasing number of outbreaks we have faced in recent times. We have a vast repository of genomic sequences from various species, including [...] Read more.

The classification of genomic sequences is a crucial area of research in the field of virology. This is due to the increasing number of outbreaks we have faced in recent times. We have a vast repository of genomic sequences from various species, including humans, animals, plants, bacteria, and viruses, which tend to mutate and form new variants or strains. In the realm of machine learning, several models are employed for genome sequence classification. Among these are traditional algorithms such as Random Forest (RF), K-nearest neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB), each offering unique advantages in handling genetic data. Additionally, deep learning models like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Bi-Directional LSTM networks are utilized for their robust capabilities in capturing complex patterns and dependencies within genomic sequences. In this study, we explored the application of Natural Language Processing (NLP) techniques to classify the genomic sequences. The focus of our research involves utilizing advanced large language models (LLMs) such as DNABERT, DNAGPT, and GENA LM, which are fine-tuned explicitly on the language of DNA. In this research, after a detailed analysis, we found that DNAGPT achieved an accuracy of 96%, which exceeds the performance of state-of-the-art machine learning and deep learning models. Full article

(This article belongs to the Special Issue Advanced Research on Machine Learning Algorithms in Bioinformatics)

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20 pages, 733 KiB

Open AccessArticle

Energy Optimization in Hotels: Strategies for Efficiency in Hot Water Systems

by Yarelis Valdivia Nodal, Luis Angel Iturralde Carrera, Araceli Zapatero-Gutiérrez, Mario Antonio Álvarez Guerra Plasencia, Royd Reyes Calvo, José M. Álvarez-Alvarado and Juvenal Rodríguez-Reséndiz

Algorithms 2025, 18(6), 301; https://doi.org/10.3390/a18060301 - 22 May 2025

Abstract

This paper presents a procedure for the energy optimization of domestic hot water (DHW) systems in hotels located in tropical climates that use centralized air conditioning systems. The study aims to maximize heat recovery from chillers and reduce the fuel consumption of auxiliary [...] Read more.

This paper presents a procedure for the energy optimization of domestic hot water (DHW) systems in hotels located in tropical climates that use centralized air conditioning systems. The study aims to maximize heat recovery from chillers and reduce the fuel consumption of auxiliary heaters by optimizing operational variables such as water mass flow in the primary and secondary DHW circuits and outlet temperature of the backup system. The optimization is implemented using genetic algorithms (GA), which enable the identification of the most efficient flow configurations under variable thermal demand conditions. The proposed methodology integrates a thermoenergetic model validated with real operational data and considers the dynamic behavior of hotel occupancy and water demand. The results show that the optimized strategy reduces auxiliary heating use by up to 75%, achieving annual energy savings of 8244 kWh, equivalent to 2.3 tons of fuel, and preventing the emission of 10.5 tons of CO₂. This study contributes to the design of sustainable energy systems in the hospitality sector and provides replicable strategies for similar climatic and operational contexts. Full article

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

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17 pages, 2320 KiB

Open AccessArticle

Road Obstacle Detection Method Based on Improved YOLOv5

by Pengliu Tan, Zhi Wang and Xin Chang

Algorithms 2025, 18(6), 300; https://doi.org/10.3390/a18060300 - 22 May 2025

Abstract

Road obstacle detection is essential for ensuring the smooth operation of roads and safeguarding the lives and property of travelers. However, current obstacle detection methods face challenges such as missed detections and false positives. To address these issues, an enhanced obstacle detection algorithm [...] Read more.

Road obstacle detection is essential for ensuring the smooth operation of roads and safeguarding the lives and property of travelers. However, current obstacle detection methods face challenges such as missed detections and false positives. To address these issues, an enhanced obstacle detection algorithm based on YOLOv5 (YOLOv5-EC3F) is proposed. First, an effective multi-scale feature fusion module (EMFF) is introduced to extract multi-scale features from the input feature map, providing richer semantic information and enhancing the perceptual range. Second, the SPPF module is replaced with the C3SPPF module to improve the model’s understanding of contextual information and increase its multi-scale adaptability. Experimental results demonstrate that, on the custom dataset, YOLOv5-EC3F raises the mAP by 3 percentage points to 82% and the recall by 7 percentage points to 78%, without compromising precision. This study offers a valuable optimization strategy for the practical application of road obstacle detection. Full article

(This article belongs to the Collection Traditional and Machine Learning Methods to Solve Imaging Problems)

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22 pages, 12260 KiB

Open AccessArticle

Improved Directional Mutation Moth–Flame Optimization Algorithm via Gene Modification for Automatic Reverse Parking Trajectory Optimization

by Yan Chen, Yi Chen, Yang Guo, Longda Wang and Gang Liu

Algorithms 2025, 18(6), 299; https://doi.org/10.3390/a18060299 - 22 May 2025

Abstract

Automatic reverse parking (ARP) faces challenges in finding ideal reference trajectories that avoid collisions, maintain smoothness, and minimize path length. To address this, we propose an improved directional mutation moth–flame optimization algorithm with gene modification (IDMMFO-GM). We develop a practical reference trajectory optimization [...] Read more.

Automatic reverse parking (ARP) faces challenges in finding ideal reference trajectories that avoid collisions, maintain smoothness, and minimize path length. To address this, we propose an improved directional mutation moth–flame optimization algorithm with gene modification (IDMMFO-GM). We develop a practical reference trajectory optimization model by combining cubic spline interpolation with a standardized parking plane coordinate system. To effectively address the infeasible solutions encountered when parking in a garage, we apply gene modification for collision avoidance and berthing tilt generated from the reference trajectory optimization to enhance the preservation of optimization information. Furthermore, we introduce a non-linear decreasing weight coefficient and a directional mutation strategy into the moth–flame optimization algorithm to significantly improve its overall optimization performance. Taking the automatic parking garage space No. 155 in Dalian Shell Museum as the actual vehicle test object, which is situated within Dalian Xinghai Square, test results demonstrate that the proposed algorithm achieves an accelerated optimization speed, enhanced precision in trajectory optimization, and superior tracking control performance. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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15 pages, 356 KiB

Open AccessArticle

Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics

by Sándor Szénási, Gábor Légrádi and Gábor Kovács

Algorithms 2025, 18(5), 298; https://doi.org/10.3390/a18050298 - 21 May 2025

Abstract

Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming [...] Read more.

Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming and complex task. This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. This model was used in a novel alternating method (using traditional and Machine Learning-based steps) to predict the optimal step size for each iteration. This hybrid algorithm was compared to the already-known variants. The results show that the novel hybrid method is able to find slightly better results than the original Hill Climbing method, requiring significantly fewer fitness calculations. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)

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22 pages, 1552 KiB

Open AccessArticle

A Regret-Enhanced DEA Approach to Mapping Renewable Energy Efficiency in Asia’s Growth Economies

by Chia-Nan Wang, Nhat-Luong Nhieu and Yu-Cin Ye

Algorithms 2025, 18(5), 297; https://doi.org/10.3390/a18050297 - 20 May 2025

Abstract

Renewable energy (RE) is pivotal to achieving both environmental sustainability and long-term energy security, yet systematic evidence on the efficiency of RE investment across South and Southeast Asia remains sparse. This study introduces a rejoice–regret utility cross-efficiency DEA (RRUCE-DEA) framework that fuses conventional [...] Read more.

Renewable energy (RE) is pivotal to achieving both environmental sustainability and long-term energy security, yet systematic evidence on the efficiency of RE investment across South and Southeast Asia remains sparse. This study introduces a rejoice–regret utility cross-efficiency DEA (RRUCE-DEA) framework that fuses conventional quantitative efficiency measurement with the behavioral insights of regret theory. Applying the model to 16 countries shows India as the benchmark for efficient RE investment allocation, followed closely by Pakistan and Indonesia. The Philippines, Malaysia, and Vietnam also post strong results, whereas Sri Lanka and Thailand reveal moderate performance with clear room for improvement. At the lower end of the spectrum, Cambodia, Myanmar, and Afghanistan encounter significant hurdles that must be overcome to achieve a successful clean energy transition. A sensitivity analysis further explores how variations in the regret aversion and rejoice–regret coefficients affect the RRUCE-DEA outcomes. The findings provide actionable guidance for policymakers and investors seeking to channel resources toward a cleaner, more sustainable regional energy portfolio. Full article

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

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22 pages, 1716 KiB

Open AccessArticle

Benchmarking Multiple Large Language Models for Automated Clinical Trial Data Extraction in Aging Research

by Richard J. Young, Alice M. Matthews and Brach Poston

Algorithms 2025, 18(5), 296; https://doi.org/10.3390/a18050296 - 20 May 2025

Abstract

Large-language models (LLMs) show promise for automating evidence synthesis, yet head-to-head evaluations remain scarce. We benchmarked five state-of-the-art LLMs—openai/o1-mini, x-ai/grok-2-1212, meta-llama/Llama-3.3-70B-Instruct, google/Gemini-Flash-1.5-8B, and deepseek/DeepSeek-R1-70B-Distill—on extracting protocol details from transcranial direct-current stimulation (tDCS) trials enrolling older adults. A multi-LLM ensemble pipeline ingested ClinicalTrials.gov records, [...] Read more.

Large-language models (LLMs) show promise for automating evidence synthesis, yet head-to-head evaluations remain scarce. We benchmarked five state-of-the-art LLMs—openai/o1-mini, x-ai/grok-2-1212, meta-llama/Llama-3.3-70B-Instruct, google/Gemini-Flash-1.5-8B, and deepseek/DeepSeek-R1-70B-Distill—on extracting protocol details from transcranial direct-current stimulation (tDCS) trials enrolling older adults. A multi-LLM ensemble pipeline ingested ClinicalTrials.gov records, applied a structured JSON schema, and generated comparable outputs from unstructured text. The pipeline retrieved 83 aging-related tDCS trials—roughly double the yield of a conventional keyword search. Across models, agreement was almost perfect for the binary field brain stimulation used (Fleiss κ ≈ 0.92) and substantial for the categorical primary target (κ ≈ 0.71). Numeric parameters such as stimulation intensity and session duration showed excellent consistency when explicitly reported (ICC 0.95–0.96); secondary targets and free-text duration phrases remained challenging (κ ≈ 0.61; ICC ≈ 0.35). An ensemble consensus (majority vote or averaging) resolved most disagreements and delivered near-perfect reliability on core stimulation attributes (κ = 0.94). These results demonstrate that multi-LLM ensembles can markedly expand trial coverage and reach expert-level accuracy on well-defined fields while still requiring human oversight for nuanced or sparsely reported details. The benchmark and open-source workflow set a solid baseline for future advances in prompt engineering, model specialization, and ensemble strategies aimed at fully automated evidence synthesis in neurostimulation research involving aging populations. Overall, the five-model multi-LLM ensemble doubled the number of eligible aging-related tDCS trials retrieved versus keyword searching and achieved near-perfect agreement on core stimulation parameters (κ ≈ 0.94), demonstrating expert-level extraction accuracy. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))

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31 pages, 2969 KiB

Open AccessArticle

A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem

by Fuyuan Zheng, Kanglong Cheng, Kai Yang, Ning Li, Yu Lin and Yiwen Zhong

Algorithms 2025, 18(5), 295; https://doi.org/10.3390/a18050295 - 19 May 2025

Abstract

The harmony search (HS) algorithm is a novel metaheuristic which has been widely used to solve both continuous and discrete optimization problems. In order to improve the performance and simplify the implementation of the HS algorithm for solving the 0-1 knapsack problem (0-1KP), [...] Read more.

The harmony search (HS) algorithm is a novel metaheuristic which has been widely used to solve both continuous and discrete optimization problems. In order to improve the performance and simplify the implementation of the HS algorithm for solving the 0-1 knapsack problem (0-1KP), this paper proposes a heuristics-guided simplified discrete harmony search (SDHS) algorithm which does not use random search operator and has only one intrinsic parameter, harmony memory size. The SDHS algorithm uses a memory consideration operator to construct a feasible solution, and then the constructed solution is further enhanced by a solution-level pitch adjustment operator. Two heuristics, the profit–weight ratio of an item and the profit of an item, are used to greedily guide the memory consideration operator and the solution-level pitch adjustment operator, respectively. In the memory consideration operator, items are considered in non-ascending order of profit–weight ratio assigned from the harmony memory. In the solution-level pitch adjustment operator, items not in the knapsack are attempted to be selected in non-ascending order of profit. The SDHS algorithm outperforms several state-of-the-art algorithms, with an average improvement of 0.55% in the quality of solutions on large problem instances. Full article

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

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25 pages, 5349 KiB

Open AccessReview

The Scientific Landscape of Hyper-Heuristics: A Bibliometric Analysis Based on Scopus

by Helen C. Peñate-Rodríguez, Gilberto Rivera, J. Patricia Sánchez-Solís and Rogelio Florencia

Algorithms 2025, 18(5), 294; https://doi.org/10.3390/a18050294 - 19 May 2025

Abstract

Hyper-heuristics emerged as a broader metaheuristic framework to address the limitations of traditional optimization heuristics. By abstracting the design of low-level heuristics, hyper-heuristics offer a flexible and adaptable approach to solving complex problems. This study conducts a bibliometric analysis of the hyper-heuristic-algorithms-related literature [...] Read more.

Hyper-heuristics emerged as a broader metaheuristic framework to address the limitations of traditional optimization heuristics. By abstracting the design of low-level heuristics, hyper-heuristics offer a flexible and adaptable approach to solving complex problems. This study conducts a bibliometric analysis of the hyper-heuristic-algorithms-related literature indexed in the Scopus database to map its evolution, identify key research trends, and pinpoint influential authors and journals. The study encompasses document growth over time, predominant author keywords, high-impact journals, and prolific authors ranked by publication count and citation impact. A detailed examination of author keywords unveils the core research themes within the hyper-heuristic domain. The findings of this study provide valuable insights into the current literature in hyper-heuristic research and offer guidance for novice and experienced researchers. Full article

(This article belongs to the Special Issue Engineering Applications of Optimization Algorithms: Heuristics, Metaheuristics, and Hyperheuristics)

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18 pages, 1392 KiB

Open AccessArticle

A Simulation of Contact Graph Routing for Mars–Earth Data Communication

by Basuki Suhardiman, Kuntjoro Adji Sidarto and Novriana Sumarti

Algorithms 2025, 18(5), 293; https://doi.org/10.3390/a18050293 - 19 May 2025

Abstract

In this study, we develop a simulation of Contact Graph Routing (CGR) for data communication between Mars, Earth, and relay satellites. Due to the changing of the satellites’ distances to Mars and Earth, respectively, there are specific contact windows between NASA’s Mars rovers [...] Read more.

In this study, we develop a simulation of Contact Graph Routing (CGR) for data communication between Mars, Earth, and relay satellites. Due to the changing of the satellites’ distances to Mars and Earth, respectively, there are specific contact windows between NASA’s Mars rovers and orbiting relay satellites, and specific contact windows between these relay satellites and NASA’s global system of antennas on Earth. The barrier in communication develops delays caused by link propagation, so it needs a Delay Tolerant Network (DTN) for routing networks among the nodes (satellites and antennas), which is the concept of storing and forwarding data whenever the windows are open. We construct an efficient algorithm for CGR, which puts all objects into a general framework of numbered nodes, so that we can easily develop another application of a network with a larger number of nodes. Simulated data are generated randomly to mimic the unpredicted data volumes that are sent from Mars to Earth. We construct some cases involving delivering data for one Martian day, and the simulation performs well in carrying, storing, and forwarding data from Mars to Earth, even though the relay satellites are not able to contact Earth for a period of time. Full article

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

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14 pages, 1771 KiB

Open AccessArticle

Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket

by Xianyang Yang, Wei Shu, Huoping Wang, Haifeng Li, Yi Wang, Di Zhang, Jiayu Liu, Deyang Wang and Wangsui Xiao

Algorithms 2025, 18(5), 292; https://doi.org/10.3390/a18050292 - 19 May 2025

Abstract

The offshore oil engineering jacket is a giant super-heavy steel frame structure with dimensions in the hundreds of meters. A high-precision free station control network is usually arranged around it to ensure construction accuracy. However, as the jacket is gradually assembled, its extreme [...] Read more.

The offshore oil engineering jacket is a giant super-heavy steel frame structure with dimensions in the hundreds of meters. A high-precision free station control network is usually arranged around it to ensure construction accuracy. However, as the jacket is gradually assembled, its extreme weight will cause the widespread deformation of the surrounding ground surface, and each control point may be affected to varying degrees, resulting in the non-uniform deformation of the entire network. For adjustments of control networks in the subsequent phases, if the same starting point as the first phase is chosen without careful analysis, the starting points’ non-uniform deformation will degrade the whole network’s accuracy. Considering the particularities of the free station control network, this paper proposes an adjustment algorithm consisting of a three-step analytical method. Firstly, the initial coordinates of the points of the current phase are obtained through classical free network adjustment; second, stable and unstable points are identified via coordinate similarity transformation between the current and the first phase; and finally, quasi-stable adjustment is conducted. The experimental data analysis of a jacket control network shows that this method can effectively identify stable and unstable points, thereby ensuring construction accuracy and jacket stability. Full article

(This article belongs to the Special Issue Algorithms and Application for Spatiotemporal Data Processing)

23 pages, 4463 KiB

Open AccessArticle

Dual-Priority Delayed Deep Double Q-Network (DPD3QN): A Dueling Double Deep Q-Network with Dual-Priority Experience Replay for Autonomous Driving Behavior Decision-Making

by Shuai Li, Peicheng Shi, Aixi Yang, Heng Qi and Xinlong Dong

Algorithms 2025, 18(5), 291; https://doi.org/10.3390/a18050291 - 19 May 2025

Abstract

The behavior decision control of autonomous vehicles is a critical aspect of advancing autonomous driving technology. However, current behavior decision algorithms based on deep reinforcement learning still face several challenges, such as insufficient safety and sparse reward mechanisms. To solve these problems, this [...] Read more.

The behavior decision control of autonomous vehicles is a critical aspect of advancing autonomous driving technology. However, current behavior decision algorithms based on deep reinforcement learning still face several challenges, such as insufficient safety and sparse reward mechanisms. To solve these problems, this paper proposes a dueling double deep Q-network based on dual-priority experience replay—DPD3QN. Initially, the dueling network is integrated with the double deep Q-network, and the original network’s output layer is restructured to enhance the precision of action value estimation. Subsequently, dual-priority experience replay is incorporated to facilitate the model’s ability to swiftly recognize and leverage critical experiences. Ultimately, the training and evaluation are conducted on the OpenAI Gym simulation platform. The test results show that DPD3QN helps to improve the convergence speed of driverless vehicle behavior decision-making. Compared with the currently popular DQN and DDQN algorithms, this algorithm achieves higher success rates in challenging scenarios. Test scenario I increases by 11.8 and 25.8 percentage points, respectively, while the success rates in test scenarios I and II rise by 8.8 and 22.2 percentage points, respectively, indicating a more secure and efficient autonomous driving decision-making capability. Full article

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

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