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Efficient allocation of capacitors and vehicle-to-grid integration with electric vehicle charging stations in radial distribution networks

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APPLIED ENERGY 2025年 377卷

作者： Soliman, Ismail A. Tulsky, Vladimir el-Ghany, Hossam A. Abd Elgebaly, Ahmed E. Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta Egypt NRU Moscow Power Engn Inst Elect Power Syst Dept Moscow 111250 Russia

Electric vehicles offer zero tailpipe emissions and effectively reduce the release of harmful pollutants, which are significant contributors to air pollution and climate changes. However, the increased adoption of electric vehicles presents challenges to the power grid and could create a surge in demand characterized by fast-absorbing electrical energy. This surge can affect voltage profiles and escalate energy losses within distribution lines. This study introduces an optimization framework leveraging parallel search real-coded genetic algorithms (PSRCGA) for the efficient allocation and sizing of fast charging stations, vehicle-to-grid integration, and capacitors utilization. The optimization aims to enhance grid profitability by minimizing capacitor costs and optimizing power quality metrics through a multi-objective function. Various constraints such as power balance, bus voltage, current flow limits, and capacitor bank specifications are considered in the optimization process. By employing the PSRCGA, the study proposes a fitness function that effectively incorporates the objective function with the constraints. The proposed framework is validated through diverse scenarios applied to IEEE 33-bus and 69-bus systems, showcasing its efficacy in optimizing system performance.

关键词： Electric vehicle charging stations Distribution systems optimization algorithms Vehicle to grid Power quality Capacitors allocation

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AuGQ: Augmented quantization granularity to overcome accuracy degradation for sub-byte quantized deep neural networks

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APPLIED INTELLIGENCE 2025年第7期55卷 1-20页

作者： Mujtaba, Ahmed Lee, Wai Kong Ko, Byoung Chul Chang, Hyung Jin Hwang, Seong Oun Gachon Univ Dept IT Convergence Engn Seongnam 13120 South Korea Gachon Univ Dept Comp Engn Seongnam 13120 South Korea Keimyung Univ KMU Dept Comp Engn Daegu South Korea Univ Birmingham Sch Comp Sci Birmingham England

Deployment of neural networks on IoT devices unleashes the potential for various innovative applications, but the sheer size and computation of many deep learning (DL) networks prevented its widespread. Quantization mitigates this issue by reducing model precision, enabling deployment on resource-constrained edge devices. However, at extremely low bit-widths, such as 2-bit and 4-bit, the aggressive compression leads to significant accuracy degradation due to the reduced representational capacity of the neural network. A critical aspect of effective quantization is identifying the range of real values (FP32) that impact model accuracy. To address accuracy loss at sub-byte levels, we introduce Augmented Quantization (AuGQ), a novel granularity technique tailored for low bit-width quantization. AuGQ segments the range of real-valued (FP32) weight and activation distributions into small uniform intervals, applying affine quantization in each interval to enhance accuracy. We evaluated AuGQ using both post-training quantization (PTQ) and quantization-aware training (QAT) methods, achieving accuracy levels comparable to full precision (32-bit) DL networks. Our findings demonstrate that AuGQ is agnostic to the training pipeline and batch normalization folding, distinguishing it from conventional quantization techniques. Furthermore, when integrated into state-of-the-art PTQ algorithms, AuGQ necessitates only 64 training samples for fine-tuning which is 16x fewer than traditional methods. This reduction facilitates the application of high-accuracy quantization at sub-byte bit-widths, making it suitable for practical IoT deployments and enhancing computational efficiency on edge devices.

关键词： Deep Neural Networks (DNNs) Edge computing Internet-of-Things (IoT) optimization algorithms Quantization of neural networks TinyML

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Machine learning approaches for predicting mechanical properties of steel-fiber-reinforced concrete

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MATERIALS TODAY COMMUNICATIONS 2025年 45卷

作者： Huang, Huan Lei, Long Xu, Guoxin Cao, Shuangli Ren, Xiping Shaanxi Univ Int Trade & Commerce Xian 712046 Shaanxi Peoples R China Hanjiang Weihe River Water Divers Project Construc Xian 710024 Shaanxi Peoples R China

Recent advancements in machine learning enable accurate prediction of concrete's mechanical properties, offering efficiency in data analysis and reducing material waste in civil engineering. What sets this study apart from the literature is the simultaneous prediction of Compressive, Tensile, and Flexural Strength for reinforced concrete (SFRC). This integrated prediction could streamline the material selection process, enhance the durability of concrete structures, and improve performance in real-world applications such as pavements, bridges, and industrial floors. Unique variables like fiber length and content are among the input variables for predictions, which may lead to more cost-effective structural solutions. In this study, base models of the Ridge Regression and Quantile Regression were cross-validated, and the best folds of the compiled dataset for training and testing models were selected. The Sled Dog Optimizer, as a recently developed strong optimization algorithm, is utilized for fine-tuning hyperparameters of the models and enhancing prediction performance. Also, the Bayesian model combination method was employed to develop a hybrid ensemble model for reliably predicting concrete strengths by combining the capabilities of both hybrid models. Such hybrid and ensemble models are rare in the literature in this field. Also, SHAP-based explainable AI utilized for interpreting importance of variables in predictions. The hybrid ensemble model performed best in predicting Compressive Strength, achieving the lowest RMSE (3.295 MPa) and highest R2 (0.974). For Flexural and Tensile Strength, it attained R2 values of 0.986 and 0.974, respectively, with 90% and 100% of predictions showing errors below 10%. Among hybrid models, RRSD excelled, with R2 values of 0.94 for Compressive Strength and 0.98 for Flexural and Tensile Strength. These results demonstrate the reliability of machine learning for modeling SFRC properties.

关键词： Experimental concrete samples Steel-fiber-reinforced concrete Strength properties Regression prediction models optimization algorithms

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Leveraging evolutionary algorithms with a dynamic weighted search space approach for fraud detection in healthcare insurance claims

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KNOWLEDGE-BASED SYSTEMS 2025年 317卷

作者： Tubishat, Mohammad Tbaishat, Dina Al-Zoubi, Ala'M. Hraiz, Abed-Elalim Habib, Maria Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Zayed Univ Coll Technol Innovat Dubai U Arab Emirates Al Zaytoonah Univ Jordan Fac Sci & Informat Technol Dept Data Sci & Artificial Intelligence Amman Jordan Univ Jordan King Abdullah II School Informat Technol Amman Jordan Univ Granada Doctoral Sch Sci Technol & Engn Granada Spain

The healthcare industry has been suffering from fraud in many facets for decades, resulting in millions of dollars lost to fictitious claims at the expense of other patients who cannot afford appropriate care. As such, accurately identifying fraudulent claims is one of the most important factors in a well-functioning healthcare system. However, over time, fraud has become harder to detect because of increasingly complex and sophisticated fraud scheme development, data unpreparedness, as well as data privacy concerns. Moreover, traditional methods are proving increasingly inadequate in addressing this issue. To solve this issue a novel evolutionary dynamic weighted search space approach (DW-WOA-SVM) is presented in the current study. The approach has different levels that work simultaneously, where the optimization algorithm is responsible for tuning the Support Vector Machine (SVM) parameters, applying the weighting procedure for the features, and using a dynamic search space to adjust the range values. Tuning the parameters benefits the performance of SVM, and the weighting technique makes it updated with importance and lets the algorithm focus on data structure in addition to optimization objectives. The dynamic search space enhances the search range during the process. Furthermore, large language models have been applied to generate the dataset to improve the quality of the data and address the lack of good dimensionality, helping to enhance the richness of the data. The experiments highlighted the superior performance of this proposed approach than other algorithms.

关键词： Medical fraud Detection Claims SVM LLM Dynamic search space Feature weighing optimization algorithms

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Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

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WATER RESOURCES MANAGEMENT 2025年第3期39卷 1089-1105页

作者： Naceur, Khaoula Ait El Khalki, El Mahdi Hadri, Abdessamad Jaffar, Oumar Brocca, Luca Saidi, Mohamed Elmehdi Tramblay, Yves Chehbouni, Abdelghani Mohammed VI Polytech Univ UM6P Int Water Res Inst Benguerir Morocco CNR Res Inst Geohydrol Protect Perugia Italy Cadi Ayyad Univ Fac Sci & Tech Earth Sci Dept Lab L3G Marrakech Morocco Univ Montpellier Espace Dev IRD Montpellier France Mohammed VI Polytech Univ UM6P Ctr Remote Sensing Applicat CRSA BenGuerir Morocco

Hydrological modeling is a crucial tool for water resources management. It becomes more important in data-scarce regions like Morocco. Therefore, accurate parameter tuning of models used in this region is vital for reliable predictions. Traditionally, the Nelder-Mead Simplex Algorithm has been used to calibrate the GR4J and MISDc models. However, this study aims to enhance the calibration process by employing Particle Swarm optimization (PSO), Nelder-Mead Simplex Algorithm (FMIN), Simulated Annealing (SA), and Genetic Algorithm (GA) for daily streamflow forecasts across 26 basins. A sensitivity analysis of their parameters was performed, along with the use of various calibration scenarios. In addition, a snow module was used in the mountainous basins. The research reveals significant sensitivity of the GR4J groundwater exchange coefficient and MISDc parameters related to soil, drainage, and snow dynamics. FMIN and PSO proved to be the most efficient, and the MISDc model outperformed GR4J. The choice of splitting scenario proved critical, and the lower model performance was attributed to discrepancies between calibration and validation periods in terms of runoff coefficients, precipitation-runoff correlations, and the distribution of dry and wet years. Integrating a snow module in both models enhanced their performance in larger basins.

关键词： Hydrological Modeling Morocco GR4J MISDc optimization algorithms Parameter Sensitivity

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A Fixed-Point Iteration Scheme for Sensitivity-Based Distributed Optimal Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2778-2785页

作者： von Esch, Maximilian Pierer Volz, Andreas Graichen, Knut Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Automatic Control D-91058 Erlangen Germany

This article presents a sensitivity-based algorithm for distributed optimal control problems (OCP) of multi-agent systems with nonlinear dynamics and state/input couplings, as they arise, for instance, in distributed model predictive control. The algorithm relies on first-order sensitivities to cooperatively solve the distributed OCP in parallel. The solutions to the resulting local OCPs are computed with a fixed-point scheme and communicated within one communication step per algorithm iteration to the neighbors. Convergence results are presented under the inexact minimization of the local OCP. The algorithm is evaluated in numerical simulations for an example system.

关键词： Heuristic algorithms Convergence Couplings Costs Trajectory Sensitivity Prediction algorithms Optimal control Vectors Predictive control Agents and autonomous systems distributed optimal control nonlinear systems optimization algorithms

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Emerging maximum power point control algorithms for PV system: review, challenges and future trends

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ELECTRICAL ENGINEERING 2025年 1-33页

作者： Siddique, Muhammad Abu Bakar Zhao, Dongya Rehman, Ateeq Ur China Univ Petr East China Coll New Energy Qingdao 266580 Peoples R China Saveetha Inst Med & Tech Sci Saveetha Sch Engn Comp Sci & Engn Chennai Tamilnadu India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India

The maximum power point tracking (MPPT) algorithms are essential for ensuring optimal energy conversion and efficient power transfer between the photovoltaic (PV) system and the load. This paper provides a comprehensive review of emerging MPPT algorithms for PV systems under different weather conditions, with a focus on their challenges and future trends. The review covers various types of converters, inverters, MPPT techniques including traditional, optimization, and artificial intelligence (AI)-based control strategies used in PV systems. The converters play a crucial role in converting the DC power generated by the PV panels into usable power that can be consumed by different loads. The paper highlights the working principle, limitations, challenges, and comparison of these techniques to choose most suitable algorithm for a specific application. Furthermore, the review discusses the future trends and enhancement in MPPT algorithms, such as the use of AI and optimization techniques to improve the performance and efficiency of PV systems. Overall, this paper provides valuable perspectives into the current state of MPPT algorithms for PV systems and the potential avenues for future research in this field.

关键词： Maximum power point tracking (MPPT) Artificial intelligence optimization algorithms Renewable energy Power converters Power inverters Energy conversion

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A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO algorithms

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ENERGIES 2025年第8期18卷 2024-2024页

作者： Alenezi, Mohammed Anayi, Fatih Packianather, Michael Shouran, Mokhtar Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Cardiff Univ Sch Engn High Value Mfg Grp Cardiff CF24 3AA Wales Libyan Ctr Engn Res & Informat Technol Bani Walid Libya

The early detection of faults in power transformers is crucial for ensuring operational reliability and minimizing system disruptions. This study introduces a novel machine learning framework that integrates Particle Swarm optimization (PSO) and Dwarf Mongoose optimization (DMO) algorithms for feature selection and hyperparameter tuning, combined with advanced classifiers such as Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVM). A 5-fold cross-validation approach was employed to ensure a robust performance evaluation. Feature extraction was performed using both Discrete Wavelet Decomposition (DWD) and Matching Pursuit (MP), providing a comprehensive representation of the dataset comprising 2400 samples and 41 extracted features. Experimental validation demonstrated the efficacy of the proposed framework. The PSO-optimized RF model achieved the highest accuracy of 97.71%, with a precision of 98.02% and an F1 score of 98.63%, followed by the PSO-DT model with a 95.00% accuracy. Similarly, the DMO-optimized RF model recorded an accuracy of 98.33%, with a precision of 98.80% and an F1 score of 99.04%, outperforming other DMO-based classifiers. This novel framework demonstrates significant advancements in transformer protection by enabling accurate and early fault detection, thereby enhancing the reliability and safety of power systems.

关键词： power transformer protection fault detection machine learning classifiers early fault diagnosis optimization algorithms feature extraction

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Bioinspiration-based deep learning algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2025年第2期40卷 979-994页

作者： Cifci, Mehmet Akif Canatalay, Peren Jerfi Arslan, Emrah Kausar, Samina TU Wien Inst Comp Technol A-1040 Vienna Austria Bandirma Onyedi Eylul Univ Dept Comp Engn TR-10200 Balikesir Turkiye Klaipedos Valstybine Kolegija Higher Educ Inst Engn & Informat Dept LT-92294 Klaipeda Lithuania Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye Istanbul Aydin Univ Fac Engn Dept Comp Engn TR-34295 Istanbul Turkiye Shanghai Univ Shanghai Peoples R China Univ Kotli Azad Jammu & Kashmir Kotli Azad Kashmir Pakistan

This paper presents the Infection Susceptible Artificial Intelligence optimization Model (SIMO, susceptible-infected- removed model optimizer), an innovative learned heuristic inspired by biological systems and Deep Learning (DL) techniques. The SIMO optimization algorithm estimates the susceptibility of the population to infection, active infections and the recovering population at any point in time, inspired by the epidemiological partition model with Infection-Sensitive Artificial Intelligence. SIMO integrates the IA method into the initialisation method and parameter tuning components to improve the search process, so that it can exhibit intelligent and autonomous behaviour. The integration of the IO facilitates the generation of initial solutions based on neural models, which allows the algorithm to be guided towards efficient, effective and robust search results. This approach improves the performance of the algorithm by obtaining high-level solutions, allowing it to converge faster, increasing its robustness and reducing its computational requirements. Two datasets from the 2017 IEEE Congress on Evolutionary Computing (CEC 2017) benchmarking functions are used to validate the effectiveness of the SIMO algorithm and the experimental results are compared with innovative algorithms. Detailed comparisons show that SIMO outperforms many similar models, offering high performance solutions using fewer control parameters. Furthermore, the performance of SIMO is adapted to real-life problems. The results clearly show that integrating the learning process into SIMO provides superior accuracy and computational efficiency compared to other optimization approaches in the existing literature.

关键词： SIMO neural learning optimization algorithms engineering design optimization metaheuristics deep learning

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Efficient Hybrid-Robust Approach for Cancer Biomarker Discovery Using Omics Data

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IEEE ACCESS 2025年 13卷 51130-51149页

作者： Sid, Karima Zertal, Soumia Batouche, Mohamed Zerabi, Soumeya Oum El Bouaghi Univ Dept Math & Comp Sci Oum El Bouaghi 04000 Algeria Abdelhamid Mehri Constantine 2 Univ Lab Data Sci Comp & Artificial Intelligence LISIA Constantine 25016 Algeria Oum El Bouaghi Univ Dept Math & Comp Sci Lab Artificial Intelligence & Autonomous Things LI Oum El Bouaghi 04000 Algeria Princess Nourah bint Abdulrahman Univ Dept Informat Technol CCIS RC Riyadh 11671 Saudi Arabia Abdelhamid Mehri Constantine 2 Univ Dept Comp Sci & Its Applicat Lab Data Sci Comp & Artificial Intelligence LISIA Constantine 25016 Algeria

DNA Microarray datasets, also known as "omics" data, are important for the diagnosis of numerous diseases, including cancer and tumors. In the analysis of these data, feature selection techniques and classification algorithms are the workhorse for choosing candidate genes that serve as cancer biomarkers. However, microarray datasets present a challenge;they contain a greater number of features than the samples, which affects the performance of algorithms used in the analysis process. In order to extract precise information, it is necessary to employ a method that is both robust and performant. This paper emphasizes the importance of accurate and stable gene selection for the discovery of knowledge derived from high-dimensional data. A novel hybrid framework was put forth for consideration, comprising three distinct stages: Clustering, Parallel Filtering, and Hybrid-Parallel optimization. In each step, a combination of techniques and algorithms is used to improve the results in terms of stability and/or accuracy. The proposal is evaluated and tested according to different scenarios;using thirteen gene expression datasets and two classifiers: Artificial Neural Network (ANN) and Na & iuml;ve Bayes (NB). Comparison with related work demonstrates the efficacy of this approach, which enhances classification accuracy and stability while reducing the number of selected genes.

关键词： Feature extraction Stability criteria Classification algorithms Cancer Clustering algorithms Gene expression Accuracy Approximation algorithms Metaheuristics Training Biomarker discovery microarray data feature selection classification optimization algorithms Markov blanket approximation

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