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e-Journal of Nondestructive Testing 2025年第2期30卷

作者： Zhou, Yuzhong Schneider, Linda-Sophie Sun, Yipeng Maier, Andreas Fraunhofer EZRT Flugplatzstraße 75 Fürth Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

This study addresses the challenge of applying analytical methods for Cone Beam Computed Tomography (CBCT) reconstructions along arbitrary trajectories instead of iterative methods. Traditional analytical methods like Filtered Back Projection (FBP) often fail to adequately process CBCT images due to the intricate and varied paths involved. Although iterative methods are a common solution, they require substantial computational time and resources. To address these challenges, this paper proposes two approaches, the first approach enhances the traditional FBP algorithm by using deep learning to train an optimized filter before reconstruction. The second approach improves upon the Backprojection then Filtering (BPF) algorithm by first reconstructing and then applying a deep learning-trained filter. Both methods significantly optimize the initial reconstruction results and enhance efficiency, offering promising improvements over existing iterative reconstruction techniques. © 2025, null. All rights reserved.

关键词： optimization algorithms

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Formalization of algorithms for optimization with Block Structures

arXiv

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arXiv 2025年

作者： Li, Chenyi Wang, Zichen Bai, Yifan Duan, Yunxi Gao, Yuqing Hao, Pengfei Wen, Zaiwen School of Mathematical Sciences Peking University China School of Mathematics and Statistics Xi’an Jiaotong University China School of Mathematical Sciences Shanghai Jiao Tong University China School of Mathematics Statistics and Mechanics Beijing University of Technology China Institut für Numerische Simulation Universität Bonn Germany School of Mathematical Sciences Beijing Normal University China Beijing International Center for Mathematical Research Peking University China

Block-structured problems are central to advances in numerical optimization and machine learning. This paper provides the formalization of convergence analysis for two pivotal algorithms in such settings: the block coordinate descent (BCD) method and the alternating direction method of multipliers (ADMM). Utilizing the type-theory-based proof assistant Lean4, we develop a rigorous framework to formally represent these algorithms. Essential concepts in nonsmooth and nonconvex optimization are formalized, notably subdifferentials, which extend the classical differentiability to handle nonsmooth scenarios, and the Kurdyka–Lojasiewicz (KL) property, which provides essential tools to analyze convergence in nonconvex settings. Such definitions and properties are crucial for the corresponding convergence analyses. We formalize the convergence proofs of these algorithms, demonstrating that our definitions and structures are coherent and robust. These formalizations lay a basis for analyzing the convergence of more general optimization algorithms. © 2025, CC BY.

关键词： optimization algorithms

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Particle Swarm optimization: Advances, Applications, and Experimental Insights

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Computers, Materials & Continua 2025年第2期82卷 1539-1592页

作者： Laith Abualigah Computer Science Department Al al-Bayt UniversityMafraq25113Jordan

Particle Swarm optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different *** paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other *** six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the *** results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of *** results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the *** review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.

关键词： Particle swarm optimization(PSO) optimization algorithms data mining machine learning engineer-ing design energy systems healthcare applications robotics comparative analysis algorithm performance evaluation

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Estimation of PEMFC optimal parameters based on an improved butterfly optimization algorithm

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PHYSICAL CHEMISTRY CHEMICAL PHYSICS 2025年第3期27卷 1483-1493页

作者： Sun, Zhe Song, Yan Du, Yijun Sun, Zhixin Nanjing Univ Posts & Telecommun Post Ind Technol Res & Dev Ctr State Posts Bur Internet Things Technol Nanjing 210023 Peoples R China Nanjing Inst Technol Sch Automat Nanjing 211167 Peoples R China

This paper introduces a novel butterfly optimization algorithm, called the spiral search and dynamic crossover based butterfly optimization algorithm (SCBO), for parameter estimation in proton exchange membrane fuel cell (PEMFC) models. To enhance the global performance of the butterfly algorithm, a spin-search strategy is incorporated to expand its exploration range, while an adaptive factor is introduced to strike a balance between exploration and exploitation. Additionally, a dynamic crossover operation is integrated to enhance solution diversity, addressing the algorithm's tendency to converge to local optima. Extensive experimentation on benchmark functions in comparison with common optimization algorithms demonstrates that SCBO outperforms others in terms of convergence accuracy and speed. Finally, we employ SCBO for parameter identification in a PEMFC model, showcasing its superior results and its ability to capture the model's dynamics when compared to other algorithms.

关键词： optimization algorithms

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Holistic optimization of electric vehicle charging stations in radial power systems with V2G and DG integration considering fault repairing periods

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APPLIED ENERGY 2025年 385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 Moscow Power Engn Inst Elect Power Syst Dept NRU Moscow 111250 Russia

Adopting electric vehicles is pivotal for mitigating environmental impacts and enhancing energy sustainability by reducing emissions and fossil fuel reliance in transportation systems. The integration of electric vehicles significantly impacts power grids and increases complexity but offers opportunities for grid stabilization through vehicle-to-grid (V2G) technology. Grid performance can be improved by integrating renewable energy sources (RESs) and reactive power compensation devices. This paper presents a proposed multi-objective function solved using parallel search real-coded genetic algorithm (PSRCGA) for obtaining the optimal placement of electric vehicle charging stations (EVCSs), V2G sharing points, photovoltaic systems (PV), wind farms, and capacitor banks. The multi-objective function aims to minimize the cost function and total energy and maximize power quality indices. The power quality indices are improved within constraints including the voltage stability and voltage deviation index. The constraints are merged with the multi-objective function within a fitness function, implemented considering fault repairing periods (FRP). Load flow analysis is applied on the IEEE 33-bus radial test system and the algorithm is verified on a real system. The system is divided into various zones to depict streets/regions/population distribution. The results affirm the algorithm's reliability across different scenarios and system configurations. Integrating RESs of 40 % in the IEEE 33-bus system decreases the annual active energy loss to 575.40 MWh with simultaneously inserting capacitor banks of 43.24% reduces the reactive energy loss to 415.10 Mvarh. The system minimum voltage is maintained at 0.903 pu during system reconfiguration in FRP, which is within the allowable voltage limits

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

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Online Interior-Point Methods for Time-Varying Equality-Constrained optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2636-2643页

作者： Lupien, Jean-Luc Shames, Iman Lesage-Landry, Antoine Polytech Montreal Dept Elect Engn MILA Montreal PQ H3T 1J4 Canada GERAD Montreal PQ H3T 1J4 Canada Australian Natl Univ Sch Engn CIICADA Lab Canberra ACT 2601 Australia

An important challenge in the online convex optimization (OCO) setting is to incorporate generalized inequalities and time-varying constraints. The inclusion of constraints in OCO widens the applicability of such algorithms to dynamic and safety-critical settings such as the online optimal power flow (OPF) problem. In this work, we propose the first projection-free OCO algorithm admitting time-varying linear constraints and convex generalized inequalities: the online interior-point method for time-varying equality constraints (OIPM-TEC). We derive simultaneous sublinear dynamic regret and constraint violation bounds for OIPM-TEC under standard assumptions. For applications where a given tolerance around optima is accepted, we employ an alternative OCO performance metric-the epsilon-regret-and a more computationally efficient algorithm, the epsilon-OIPM-TEC that possesses sublinear bounds under this metric. Finally, we showcase the performance of these two algorithms on an online OPF problem and compare them to another OCO algorithm from the literature.

关键词： Heuristic algorithms optimization Vectors Measurement Approximation algorithms Convex functions Linear matrix inequalities Renewable energy sources Machine learning algorithms Load flow Machine learning online convex optimization (OCO) optimization algorithms time-varying systems

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The H5N1 algorithm: a viral-inspired optimization for solving real-world engineering problems

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ENGINEERING COMPUTATIONS 2025年第3期42卷 1024-1096页

作者： Le, Thang Xuan Bui, Thanh Tien Tran, Hoa Ngoc Univ Minho ISISE Dept Civil Engn Guimaraes Portugal Univ Transport & Commun Hanoi Vietnam

PurposeIn recent years, the development of metaheuristic algorithms for solving optimization problems within a reasonable timeframe has garnered significant attention from the global scientific community. In this work, a new metaheuristic algorithm inspired by the inflection mechanism of the avian influenza virus H5N1 in poultry and humans, taking into account its mutation mechanism, called ***/methodology/approachThis algorithm aims to explore optimal solutions for optimization problems by simulating the adaptive behavior and evolutionary process of the H5N1 virus, thereby enhancing the algorithm's performance for all types of optimization problems. Additionally, a balanced stochastic probability mechanism derived from the infection probability is presented. Using this mechanism, the H5N1 algorithm can change its phrase, including exploitation and exploration phases. Two versions of H5N1, SH5N1 and MH5N1, are presented to solve single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs).FindingsThe performance of the algorithm is evaluated using a set of benchmark functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal to solve SOPs, ZDT functions and CEC2009 has been used to demonstrate its superiority over other recent algorithms. Finally, six optimization engineering problems have been tested. The results obtained indicate that the proposed algorithm outperformed ten algorithms in SOPs and seven algorithms in ***/valueThe experimental findings demonstrate the outstanding convergence of the H5N1 algorithm and its ability to generate solutions of superior quality.

关键词： optimization algorithms Single-objective problems Multi-objective problems Meta-heuristics Engineering problems

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Strength and durability predictions of ternary blended nano-engineered high-performance concrete: Application of hybrid machine learning techniques with bio-inspired optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 148卷

作者： Vairagade, Vikrant S. Bahoria, Boskey V. Isleem, Haytham F. Shelke, Nilesh Mungle, Nischal P. Priyadarshini Coll Engn Dept Civil Engn Nagpur 440019 Maharashtra India Yeshwantrao Chavan Coll Engn Dept Civil Engn Nagpur 441110 Maharashtra India Jadara Univ Res Ctr Irbid 21110 Jordan Univ York Dept Comp Sci York YO10 5DD England Symbiosis Int Deemed Univ Symbiosis Inst Technol Nagpur Campus Pune 440008 Maharashtra India Yeshwantrao Chavan Coll Engn Dept Mech Engn Nagpur 441110 Maharashtra India

Higher-strength, longer-lived, and environmentally friendly high-performance concrete (HPC) necessitates advanced formulation of materials and predictive modeling techniques. The traditional designs of highperformance concrete often fail to optimize the mix parameters suitably, mainly from the point of sustainability view, and lack the capacity to model complex nonlinear relationships among material properties and performance metrics. The current paper introduces the integrated framework of nano-engineered materials that comprises nano-silica, graphene oxide (GO), and carbon nanotubes (CNTs) along with advanced artificial intelligence (AI) methods in the predictive modeling and optimization of high-performance concrete (HPC). The Machine Learning (ML) models consisting of Random Forest (RF), Gradient Boosting Machines (GBM), and Deep Neural Networks (DNNs) were implemented to predict compressive and tensile strengths, elastic modulus, and durability metrics such as chloride penetration and sulfate resistance sets. Dimensionality reduction techniques, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), improved model interpretability and performance sets. optimization of mix designs was carried out using bio-inspired algorithms, namely Genetic algorithms (GA) and Particle Swarm optimization (PSO), to balance mechanical performance, material efficiency, and environmental sustainability. The results suggest strength improvement up to 15% and durability up to 30% while reducing cement content and associated carbon emissions. This is the framework, which exploits artificial intelligence techniques in predictive modeling and optimization to achieve scalable and sustainable next-generation high-performance concrete designs. It is at the intersection of theoretical potential and practical application that the work is advancing the role of artificial intelligence in construction material science toward contributing to sustainable infrastructure develo

关键词： Nano-engineered materials Artificial intelligence Machine learning High-performance concrete Genetic algorithms Particle swarm optimization Sustainable construction materials Predictive modeling optimization algorithms

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Research on Multi-Strategy Fusion of the Chimpanzee optimization Algorithm and Its Application in Path Planning

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APPLIED SCIENCES-BASEL 2025年第2期15卷 608-608页

作者： He, Xing Guo, Chenxv Xian Univ Architecture & Technol XUAT Coll Informat & Control Engn 13 Yanta Rd Xian 710055 Peoples R China

In this paper, a multi-strategy enhanced chimpanzee optimization algorithm (MSEChOA) acting on path planning for delivery vehicles is proposed to achieve the goal of shortening global path lengths for the delivery unmanned vehicles and obtaining safer paths. In the initialization phase, the algorithm introduces a hybrid good point set and chaos initialization strategy, combining the advantages of both to enhance the randomness and homogeneity of the initial population. After that, it incorporates a benchmark weight strategy and Gaussian-modulated cosine factor to adaptively adjust algorithm parameters, thus balancing the global and local search capabilities and improving the search efficiency. In the end, the algorithm incorporates a global search enhancer (GEE) to further enhance the global search capability in the later phases, thereby avoiding local optima. Experiments on several benchmark test functions show that MSEChOA outperforms traditional ChOA and other optimization algorithms in optimization accuracy and convergence speed. In simulation experiments, MSEChOA shows stronger path planning ability and good computational efficiency in both simple and complex environments, proving its feasibility and superiority in the field of path planning.

关键词： delivery vehicles path planning optimization algorithms chimpanzee optimization multi-strategy optimization

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A novel intensified anaerobic co-digestion process for high-quality biomethane production using ultrasound energy: optimization and robustness tests with ML-RSM-TLBO algorithm

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ENERGY CONVERSION AND MANAGEMENT 2025年 325卷

作者： Ahmad, Aqueel Yadav, Ashok Kumar Pal, Amit Hasan, Shifa Netaji Subhas Univ Technol Mech Engn Dept New Delhi India Graph Era Dept Mech Engn Dehra Dun India Raj Kumar Goel Inst Technol Dept Mech Engn Ghaziabad India Delhi Technol Univ Mech Engn Dept New Delhi India Netaji Subhas Univ Technol Dept Management Studies New Delhi India

Biogas, a viable substitute for petroleum-based fuels, faces limitations in conventional production methods, which are slow and yield low outputs. Process intensification techniques, offer faster conversion and higher yields compared to conventional anaerobic digestion. However, the nonlinear dynamics and complex reaction kinetics involved in anaerobic digestion present significant challenges for traditional modeling approaches. To address this, machine learning (ML) has emerged as a powerful tool for capturing these complex behaviors. This study investigates the application of tree-based ensemble ML models to analyze biogas production from food waste co-digested with cow dung using an ultrasonic reactor across varying input parameters and operational conditions. Ten ML regression algorithms were assessed based on statistical performance metrics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The gradient-boosting regressor achieved superior performance with an MAE of 1.359, RMSE of 1.9932, and MAPE of 2.44 %, outperforming other models in predicting biogas yields. Further optimization of input parameters was conducted using response surface methodology (RSM) with desirability analysis, while the teaching-learningbased optimization (TLBO) algorithm tested model robustness. Optimal conditions for maximizing methane yield (69.831 %) were identified as 1500 W ultrasonic power, 52.21 min of sonication, and a reactor temperature of 56.73 degrees C. These findings were validated through confirmatory experiments, aligning closely with the RSM results, further confirming the robustness of the developed model. A comparative analysis between conventional and ultrasonic techniques demonstrated that the ultrasonic method achieved a 32.37 % higher methane yield compared to the conventional anaerobic digestion process. This study is significant for its potential to improve biogas production efficiency via process opt

关键词： Biogas production Process optimization Ultrasound technology Data analysis Predictive modeling optimization algorithms

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