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GEOENERGY SCIENCE AND ENGINEERING 2025年 244卷

作者： Yang, Huohai He, Qinghui Min, Chao Zhang, Ping Li, Hancheng Fu, Yu Li, Renze Li, Zonglin Duan, Chenglian Southwest Petr Univ Petr Engn Sch Chengdu Peoples R China Southwest Petr Univ Inst Artificial Intelligence Chengdu Peoples R China China Oil & Gas Pipeline Network Corp Beijing Peoples R China Sinopec Zhong Yuan Oilfield Branch Beijing Peoples R China

Underground natural gas storage (UNGS) plays a vital role in the peaking adjustment of gas supply. optimization of UNGS injection-production operations is essential for improving storage efficiency and capacity. This study first establishes and compares nine classical machine learning models such as physical neural network, gradient Boosting, and neural networks for prediction model preference, and finally selects extreme gradient Boosting (XG-Boost) to construct an agent model for predicting well pressure, which replaces the traditional complex mathematical model. Furthermore, a multi-objective optimization model was established after the optimization algorithm was preferred based on the agent model, with the objectives of maximizing the gas injection and extraction volume and minimizing the pressure difference between blocks by using a non-dominated sorting genetic algorithm-III (NSGA-III). We identified cases of injection-mining scheme optimization in the Wen23 large-scale UNGS and excluded substandard samples. By comparing the optimization results to the numerical model empirical scheme, the results indicate that the average differential pressure in each well zone decreased by 2% (33.3 MPa). Simultaneously, the maximum differential pressure between blocks was reduced significantly by 64%. On the other hand, after optimization at the end of gas production, the average well zone pressure increased by 6% (24.1 MPa), while the maximum differential pressure between blocks remained unchanged. This model also shows the merits of extreme conditions for injection mining, with the average value of the mean absolute error (MAE)between the optimized and numerical model schemes being 0.25. It is an effective optimization model that can assist and guide UNGS injection production to improve pressure distribution, gas diffusion range, and storage capacity.

关键词： Underground gas storage Injection-mining scheme Machine learning Agent model optimization algorithm

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Fuzzy Boosted Sooty Tern optimization (FBSTO): Fault Detection and Node Recovery Approach for WSN

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第1期38卷

作者： Chakraborty, Sujay Raghuvanshi, Ajay Singh Natl Inst Technol Dept Elect & Commun Engn Raipur Chhattisgarh India

The fault node detection and recovery is essential in WSN for improving the network lifetime and node connectivity. Ensuring the tradeoff between energy consumption and node recovery is challenged due to redundant nodes. Hence, a novel approach is developed based on the objective of automatic recovery from failure, redundant node elimination, fault node replacement, and minimalizing energy consumption. In this approach, the fuzzy boosted sooty tern optimization (FBSTO) technique is proposed for fault node detection and replacement. Based on the node density and distance, the nodes are clustered. Then, the cluster head selection process can be accomplished with a fuzzy logic approach based on distance calculation, energy consumption, and Quality of service (QoS) nodes. The node replacement is carried out through cascaded movement, which minimizes energy utilization. The efficiency of the FBSTO approach is estimated with packet delivery ratio, packet loss ratio, end-to-end delay, and energy consumption. The proposed approach reduces the end-to-end delay to 10 ms for random deployment of 100 nodes. Also, the packet delivery ratio performance of the proposed approach is 143 for 100 SNs. For existing Multi-objective Cluster Head Based Energy-aware Optimized Routing (MCH-EOR), Ant Lion optimization (ALO), Particle swarm optimization (PSO), Gray Wolf optimization (GWO), and Genetic approach (GA), the packet delivery ratio is reduced to 130, 60, 50, and 100. Compared with the existing approaches, the proposed approach provides better performance.

关键词： cascaded movement cluster head selection clustering fault node detection fuzzy logic node replacement optimization algorithm redundant node

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Enhancing Federated Learning in Connected and Autonomous Vehicles Through Cost optimization and Advanced Model Selection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 5276-5289页

作者： Cai, Xuelian Zhao, Pincan Liu, Sha Fu, Yuchuan Li, Changle Yu, Fei Richard Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

With the rapid evolution of vehicular network technology, the integration of Machine Learning (ML) with Connected and Autonomous Vehicles (CAVs) presents both remarkable opportunities and formidable challenges. This paper addresses the crucial need for efficient ML model training in the context of Federated Learning (FL) within vehicular networks. Recognizing the limitations imposed by the tradeoff between the high energy cost at the local level with the performance problem at the global level, we propose an innovative approach that harmonizes cost optimization with strategic model selection. Our strategy primarily focuses on optimizing energy consumption during model training and updating at the vehicle end, thereby resolving the prevalent issue of limited end-user participation in FL due to high energy demands. Additionally, we introduce an advanced model selection method, prioritizing local model uploads and adaptively allocating bandwidth to clients with more extensive training data. This method enhances the efficiency and reliability of model updates, ensuring robust global model performance. We validate our approach through extensive simulations, demonstrating not only improved learning performance but also a significant reduction in energy consumption among participating clients.

关键词： Costs Training Servers Adaptation models Data models optimization Computational modeling Federated learning Energy consumption Autonomous vehicles connected and autonomous vehicle cost analysis optimization algorithm model selection

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Optimizing Well Trajectories for Enhanced Oil Production in Naturally Fractured Reservoirs: Integrating Particle Swarm optimization with an Innovative Semi- Analytical Model Framework

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SPE JOURNAL 2025年第2期30卷 957-975页

作者： Shu, Jin Han, Guoqing Yue, Zhenduo Cheng, Lei Dong, Ya Liang, Xingyuan China Univ Petr Beijing Peoples R China

Well trajectory optimization is a crucial component in the drilling engineering of naturally fractured reservoirs. The complex heterogeneity and anisotropy of such reservoirs significantly affect the pressure drop distribution within the well and, consequently, the oil well's output, impacting the economic benefits of the well. Therefore, optimizing the well segment trajectory is key to efficient reservoir development. However, traditional well trajectory optimization methods primarily focus on geological structures and drilling engineering costs, often overlooking future production benefits of the oil well. This paper proposes a new method that first establishes a semi- analytical production prediction model capable of describing complex well trajectories. Although the semi- analytical model has unique advantages in well trajectory description, it typically treats the reservoir as a homogeneous entity, which complicates handling complex reservoir characteristics. To overcome this limitation, we combined optimization algorithms and neural networks to construct a framework for addressing reservoir heterogeneity (Semianalytical Model Framework for Unconventional Wells in Heterogeneous Reservoirs, USAMF-HR), enhancing the semi- analytical model's ability to describe reservoir heterogeneity. Building on this framework, we applied the particle swarm optimization (PSO) algorithm and introduced constraints on the rationalization of initial well trajectories, as well as limits on particle movement speed and displacement, with the maximization of net present value (NPV) as the objective function, to optimize well trajectory coordinates. Through specific case analysis, the reasonableness and practicality of this method have been verified. The results show that this method can quickly and effectively plan the optimal well trajectory, significantly increasing productivity while reducing costs.

关键词： petroleum geology trajectory design complex reservoir geologist well trajectory well planning machine learning optimization optimization algorithm usamf-hr

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Smart cities optimization using computational intelligence in power-constrained IoT sensor networks

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Darabkh, Khalid A. Al-Akhras, Muna Univ Jordan Sch Engn Dept Comp Engn Amman 11942 Jordan

This paper introduces the Innovative Clustering Energy Efficient Equilibrium Optimizer-based Multi-Hop Routing Protocol (ICEE-EO-MHRP) for addressing the energy constraint in Internet of Things (IoT) network clustering utilizing the Equilibrium Optimizer (EO), a yet efficient computational intelligence method that is used for selecting Designated Cluster Head (DCH) and Backup DCH (BDCH). Additionally, ICEE-EO-MHRP deals with the IoT energy problem by incorporating a novel cost function that ends up of selecting Designated Relays (DRs) and backup DRs for the purpose of forwarding the traffic towards the sink node. Our protocol substantially reduces messages' exchanges between IoT Sensor Nodes (SNs) by making the replacement of DCH and BDCH dependent on their energy levels dropping below a threshold. To ensure a balanced communication load and efficient scheduling, an innovative deterministic distributed-time division multiple access system is employed. Not only to this extent, but we address data redundancy issue, raised among those quite adjacent SNs, and accordingly propose an efficient management that guarantees having a coherent protocol. In addition to that, device and link failures are professionally addressed by suggesting recovery mechanisms that optimize the proposed protocol. Dealing with these impairments puts our approach well ahead of the competition since it addresses the most practical issues and scenarios, particularly those with challenging environmental constraints. The simulation results demonstrate primarily that our protocol significantly improves the network lifetime by 157.83 % and 109.81 % in comparison to Particle Swarm optimization and Tabu Search (Tabu-PSO) and Energy-Efficient CH Selection by Improved Sparrow Search algorithm utilizing Differential Evolution (EECHS-ISSADE), respectively. Comparing ICEE-EO-MHRP to Tabu-PSO and EECHS-ISSADE reveals improvements in residual energy of 335.87 % and 230.05 %, respectively. Furthermore, in compar

关键词： IoT Clustering optimization algorithm EO Routing network lifetime Efficient energy Load balancing Average delay

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Recent advances of helical baffle heat exchangers: Principles, structural optimization, application and beyond

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APPLIED THERMAL ENGINEERING 2025年 274卷

作者： Cheng, Lingyan Liu, Zhan Gu, Huaduo Wu, Jiafeng Chen, Yaping Sunden, Bengt China Univ Min & Technol Sch Mech & Civil Engn State Key Lab Intelligent Construct & Hlth Operat Xuzhou 221116 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong 999077 Peoples R China Southeast Univ Engn Res Ctr BEEE Sch Energy & Environm Minist Educ Nanjing 210096 Peoples R China Lund Univ BS Heat Transfer & Fluid Flow S- 26253 Angelholm Sweden

Due to high heat transfer efficiency and low flow resistance, helical baffle heat exchangers (HBHEs) have attracted much attention and are widely utilized in industrial engineering. Aiming at efficient performances and optimal design parameters, this review provides a unified view of shell-side heat transfer enhancement techniques for HBHEs and outlines a comprehensive optimization process, including helical baffle design, shell-side fluid optimization, and advanced algorithm application. We summarize the evaluation indices for shell-side heat transfer enhancement effects, including heat transfer coefficient, pressure drop, Nusselt number, friction factor, and comprehensive performance indices. Eventually, we point out the potential challenges in terms of heat transfer improvement, pressure drop control, and fabrication processes, as well as future research directions in this field.

关键词： Helical baffle heat exchangers Heat transfer performance Shell-side optimization Baffle structures optimization algorithm

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CCHP-Type Micro-Grid Scheduling optimization Based on Improved Multi-Objective Grey Wolf Optimizer

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Energy Engineering 2025年第3期122卷 1137-1151页

作者： Yu Zhang Sheng Wang Fanming Zeng Yijie Lin College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm *** reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy *** proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local *** the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable ***,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and *** analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to ***,it enhances user satisfaction and ensures the stable operation of the micro-grid.

关键词： Multi-objective optimization algorithm hybrid energy storage micro-grid CCHP

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Concrete crack detection using ridgelet neural network optimized by advanced human evolutionary optimization

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Lin, Yongqing Ahmadi, Mehdi Alnowibet, Khalid A. Bukhari, Fawzy A. Beijing Jiaotong Vocat Tech Coll Beijing Peoples R China King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Ankara Yildirim Beyazit Univ AYBU TR-06010 Ankara Turkiye

Concrete frameworks require strong structural integrity to ensure their durability and performance. However, they are disposed to develop cracks, which can compromise their overall quality. This research presents an innovative crack diagnosis algorithm for concrete structures that utilizes an optimized Deep Neural Network (DNN) called the Ridgelet Neural Network (RNN). The RNN model was then adjusted with a new advanced version of the Human Evolutionary optimization (AHEO) algorithm that is introduced in this study. The AHEO as a new method combines human intelligence and evolutionary principles to optimize the RNN model. To train the model, an image dataset has been used, consisting of labeled images categorized as either "cracks" or "no-cracks". The AHEO algorithm has been employed to refine the network's weights, adjust the output layer for binary classification, and enhance the dataset through stochastic rotational augmentation. The effectiveness of the RNN/AHEO model was evaluated using various metrics and compared to existing methods. The model's performance is evaluated by metrics such as accuracy, precision, recall, and F1-score, and is compared to existing methods including CNN, CrackUnet, R-CNN, DCNN, and U-Net, achieving an accuracy of 99.665% and an F1-score of 99.035%. The results demonstrated that the RNN/AHEO model outperformed other approaches in detecting concrete cracks. This innovative solution provides a robust method for maintaining the structural integrity of concrete frameworks.

关键词： Concrete Crack detection Ridgelet neural network Deep neural network Advanced human evolutionary optimization Structural integrity Image dataset Classification optimization algorithm

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Advancing equipment management for construction: introducing a new model for cost, time and quality optimization

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CONSTRUCTION INNOVATION-ENGLAND 2025年

作者： Alshboul, Odey Al-Shboul, Khaled Shehadeh, Ali Tatari, Omar Hashemite Univ Fac Engn Dept Civil Engn Zarqa Jordan Jordan Univ Sci & Technol Dept Nucl Engn Irbid Jordan Yarmouk Univ Hijjawi Fac Engn Technol Dept Civil Engn Irbid Jordan Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL USA

PurposeThis paper aims to improve the selection process of heavy machinery in construction projects by developing an advanced optimization technique using the Improved Particle Swarm optimization algorithm (IPSOM). The main objectives of such a study are to optimize the key parameters of time and cost while ensuring adherence to a predefined quality benchmarks, thereby facilitating more informed and balanced decision-making in construction ***/methodology/approachA rigorous methodology was applied to identify the relevant optimization parameters, combining a comprehensive literature review with consultations with industry experts. This approach identified the most influential factors affecting machinery selection, ensuring the model's applicability and relevance across different project scales and complexities. Unique to this study, the model's novelty lies in its advanced application of the IPSOM tailored to the construction industry's specific needs, offering a systematic approach to balancing time, cost and quality *** was validated through a detailed case study, which provided empirical evidence of the model's effectiveness in a real-world application. The study introduces a groundbreaking approach to optimizing equipment selection in highway construction, with the dual aims of minimizing costs and project duration while maintaining high-quality standards. The model proposed in the study saved 53% time, reduced costs by 30.8% and increased quality by 26.3%, outperforming traditional equipment selection ***/valueThe case study analysis demonstrated the model's adaptability and potential as a crucial tool for decision-making in construction projects.

关键词： Highway infrastructure Decision-making optimization algorithm Equipment combination Construction management Mathematical solutions

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Automatic Tuning of Linear-Quadratic-Regulation/Linear-Quadratic-Gaussian Controllers via Nonsmooth optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第4期48卷 914-924页

作者： Authie, Patrick Chaville F-92370 Ile De France France

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