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Probabilistic seismic demand analysis for bridges based on earthquake scenarios

ENGINEERING STRUCTURES 2025年 323卷

作者： Wang, Tianjia Xie, Xu Cheng, Cheng Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

The current probabilistic seismic demand analysis of structures predominantly considers the correlation between the ground motion intensity measure (IM) and structural seismic demand, but fails to effectively capture regional source characteristics. It is essential to integrate earthquake engineering with seismic response of structures using source parameters to quantify regional ground motion uncertainties and determine appropriate IMs. This study presented a probabilistic seismic demand analysis method for structures based on earthquake scenarios, characterized by 1) utilizing the stochastic finite-fault method to simulate ground motions at a specific site instead of employing multiple seismic waves from different source areas, and 2) incorporating the correlation between IMs and the key indicator of source parameters, stress drop, into the probabilistic seismic demand analysis. Taking a continuous girder bridge as an example, the performance of the stress drop and 14 individual IMs in predicting the longitudinal seismic demand and reflecting source characteristics was evaluated. The results revealed that it was difficult to establish a reliable relationship between the stress drop and structural seismic demand. Owing to the complexities of ground motion and structural parameters, the individual IMs were challenging to effectively capture their characteristics simultaneously. Therefore, composite IMs have been proposed to cover the characteristics of both the source and seismic demand. The optimal solutions were derived using a multi-objective genetic algorithm, which exhibited desirable capabilities in both aspects, providing a comprehensive guide for the seismic performance assessment of regional transportation networks.

关键词： Probabilistic seismic demand analysis Stochastic finite-fault method Intensity measure Uncertainties in ground motions multi-objective genetic algorithm

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multi-objective optimization of double-layer microchannel heat sink with trapezoidal cross-sections based on computational fluid dynamics

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 214卷

作者： Mohammadi, Rafat Dadras, Vahid Arak Univ Dept Mech Engn Arak Iran

This study presents a multi-objective optimization of a trapezoidal double-layer microchannel heat sink (TDLMCHS) to minimize both thermal resistance and pumping power. A three-dimensional fluid-solid conjugate heat transfer model, coupled with a multi-objective genetic algorithm, was employed. The optimization considered five design variables, including the flow rate ratio between the upper and lower channels and four geometric parameters related to the channel cross-sections. A parametric study explored the design space, and response surface approximation was applied to improve computational efficiency. The results showed that the optimized TDL-MCHS achieved up to a 42 % reduction in thermal resistance, though at the cost of a significant increase in pumping power. Conversely, minimizing pumping power by 42 % led to a 4 % reduction in thermal resistance. The Pareto-optimal front highlights the trade-offs between thermal performance and energy consumption, providing valuable insights for the efficient design of TDL-MCHSs in electronic cooling applications.

关键词： Double-layer microchannel heat sink Trapezoidal cross-section multi-objective genetic algorithm Thermal resistance Pumping power Electronic cooling Thermal management

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A novel multi-objective optimization model for designing a reverse supply chain network

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JOURNAL OF CLEANER PRODUCTION 2025年 499卷

作者： Momeni, Mojtaba Arab Mostofi, Amirhossein Jain, Vipul Chan, Felix T. S. Persian Gulf Univ Jam Fac Engn Bushehr Iran Auckland Univ Technol Dept Management Org & Technol Auckland New Zealand RMIT Univ Melbourne Coll Business & Law Sch Accounting Informat & Supply Chain Dept Logist & Supply Chain Management Melbourne Australia Macau Univ Sci & Technol Sch Business Dept Decis Sci Taipa Macao Peoples R China

Reverse supply chain management is one of the most effective ways to protect the environment and conserve natural resources while generating significant economic benefits for manufacturing firms. This paper examines the design of a reverse supply chain consisting of various facilities, including collection, inspection, and recycling centers, internal and external remanufacturing plants, suppliers, and secondary markets. The supply chain aims to minimize costs and carbon emissions. Additionally, the proposed model is extended to a multi-objective robust stochastic-based model to consider the uncertainty in the amount and quality of return products. Furthermore, to derive the Pareto solutions, two methods, epsilon- constraint, and the Metaheuristic algorithm of the multi-objective genetic algorithm are employed. In the multi-objective genetic algorithm, a new representation of solutions is provided, considering all dependencies between the variables and embodying the concept of robustness in the solutions of the Metaheuristic algorithms. According to the research findings, the Metaheuristic algorithm could compete with the exact method of epsilon- constraint in small-size problems, while the multi-objective genetic algorithm outperforms it in large-size problems. On the other hand, a sensitivity analysis reveals that revenue and emission-related parameters influence the level of expansion in the reverse supply chain, while the standard deviation of uncertain parameters mostly affects the robustness of the solutions. A balanced capacity throughout the supply chain is also recognized as essential for improving profitability and flexibility. Additionally, the model results showed that reducing emissions related to facilities can create opportunities for all the identified objectives within the supply chain.

关键词： Reverse supply chain Robust optimization epsilon- constraint method multi-objective genetic algorithm

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Energy, exergy, economy analysis and multi-objective optimization of SOFC/GT/SCO2 hybrid power system for ships based on zero-carbon emissions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 1430-1449页

作者： Xia, Minjie Li, Chen Yao, Shouguang Yan, Xuan Wang, Chuang Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212000 Peoples R China Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China Wuhan Univ Technol Sch Traff & Transportat Engn Wuhan 430063 Peoples R China

LNG-powered ships have gradually become the mainstream of green shipping because of the high efficiency and emissions reduction, but cannot realize zero-carbon emissions operation. Therefore, a SOFC/GT/SCO2 hybrid power system for ships based on oxygen-enriched combustion carbon capture technology is proposed to achieve efficient and zero-carbon emissions operation for ships. The system is evaluated based on the thermoeconomic method, the effects of system operating parameters on system performance are investigated, and the system is optimized based on parametric analysis utilizing a multi-objective genetic algorithm. The results demonstrate that the higher operating temperature of the reformer increases the system net output power and reduces the system electricity production cost. The system net output power is maximized and the electricity production cost is minimized when the operating temperature of SOFC is 800 degrees C. For the SCO2 cycle, the selection of appropriate operating parameters is essential for the efficient operation of the system. The system achieves 7276.182 kW net power generation, 63.00% exergy efficiency, and 0.0774$/kWh electricity production cost after multi-objective optimization.

关键词： multi-objective genetic algorithm Thermoeconomic method Oxygen-enriched combustion carbon capture technology LNG-Powered ship SOFC/GT/SCO2 hybrid power system

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multi-objective genetic algorithms, NSGA-II and SPEA2, for Document Clustering

Multi-Objective Genetic Algorithms, NSGA-II and SPEA2, for D...

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International Conference on Advanced Software Engineering and Its Applications (ASEA 2011)

作者： Lee, Jung Song Choi, Lim Cheon Park, Soon Cheol Jeonbuk Natl Univ Div Elect & Informat Engn Jeonju Jeonbuk South Korea

ISBN: (纸本)9783642272066

This paper proposes the multi-objective genetic algorithm (MOGA) for document clustering. The studied, hierarchical agglomerative algorithms. k-means algorithm and general genetic algorithm (GA) are more progressing in document clustering. However, in hierarchical agglomerative algorithms, efficiency is a problem (0(n(2)logn)), k-means algorithm depends on too much the initial centroids, and general GA can converge to the local optimal value when defining an objective function which is not suitable. In this paper, two of MOGA's algorithms, NSGA-II and SPEA2 are applied to document clustering in order to complete these disadvantages. We compare to NSGA-II. SPEA2 and the existing clustering algorithms (k-means. general GA). Our experimental results show the average values of NSGA-II and SPEA2 are about 28% higher the clustering performance than the k-means algorithm and about 17% higher the clustering performance than the general GA.

关键词： Document Clustering genetic algorithm multi-objective genetic algorithm NSGA-II SPEA2

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SENNA: Unified Hardware/Software Space Exploration for Parametrizable Neural Network Accelerators

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2025年第2期24卷 1-26页

作者： Kwon, Jungyoon Min, Yemi Egger, Bernhard Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

Parametrizable neural network accelerators enable the deployment of targeted hardware for specialized environments. Finding the best architecture configuration for a given specification, however, is challenging. A large number of hardware configurations have to be considered, and for each hardware instance, an efficient software execution plan needs to be found, leading to a vast search space. Prior work has tackled this problem by dividing the search into subproblems for individual layers of a network. There is no guarantee, however, that the overall best hardware configuration that delivers the desired end-to-end performance across the entire network is among the best individual layer configurations. This work presents SENNA, a unified hardware/software space exploration framework for parametrizable neural network accelerators. To guide the exploration toward the overall best configuration, SENNA employs a multi-objective genetic algorithm with a novel design space representation that encodes the configuration of hardware and software parameters in a single chromosome. Using the Parallel Island Model (PIM), each layer is represented by one or more individual islands each containing a separate population to simultaneously search for the best configuration across the entire network. A tailored gene migration technique enables the exchange of genes between the populations of different islands. SENNA is evaluated with three parametrizable architectures and four neural networks. The evaluation result demonstrates that SENNA achieves upto 1.92x EDP improvement compared to the State-of-the-Art. With equivalent evaluation budgets, SENNA shows 2.5x-9.3x speedup compared to an Oracle scheme and the State-of-the-Art.

关键词： HW/SW co-design reconfigurable accelerators design space exploration multi-objective genetic algorithm parallel island model

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Solar Spectrum Simulation algorithms Considering AM0G and AM1.5G

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SENSORS 2025年第5期25卷 1406-1406页

作者： Yang, Junjie Zhang, Guoyu Zhao, Bin Yang, Dongpeng Zhang, Ke Zhang, Yu Zhang, Jian Ren, Zhengwei Sun, Jingrui Wang, Lu Mo, Xiaoxu Ren, Taiyang Ren, Dianwu Peng, Zeng Yang, Songzhou Lv, Jiabo Changchun Univ Sci & Technol Sch Optoelect Engn Changchun 130022 Peoples R China Jilin Optoelect Measurement & Control Instrument E Changchun 130022 Peoples R China Minist Educ Key Lab Optoelect Measurement & Control & Opt Info Changchun 130022 Peoples R China Changchun Univ Sci & Technol State Key Lab High Power Semicond Laser Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Artificial Intelligence Changchun 130022 Peoples R China Jilin Inst Metrol Jilin Prov Key Lab Measuring Instrument & Technol Changchun 130022 Peoples R China

LED solar simulators currently face limitations in their spectral simulation capabilities, especially in terms of accurately incorporating AM0G and AM1.5G solar spectra. To this end, this study introduced a framework for an LED solar spectrum simulation algorithm that considers both AM0G and AM1.5G. This study examined the principle of solar spectrum discretization and reconstruction, established a foundation for analyzing the quality of solar spectrum reconstruction, and developed a non-dominated sorting genetic algorithm II (NSGA-II)-assisted long short-term memory (LSTM)-based solar spectrum simulation strategy. This strategy integrates a multi-objective genetic algorithm to generate training datasets and a neural network for solar spectrum simulation. A dataset generation method using the NSGA-II algorithm was implemented, which leveraged the 6500 K standard blackbody spectral curve, the spectral curve offset coefficients, and the spectral distributions of various narrowband LEDs. An LSTM-based neural network for solar spectrum simulation was developed, with the RMSE serving as the evaluation function. The analysis and selection of 29 narrowband LEDs produced 5000 solar spectrum simulation training datasets. The trained LSTM model achieved spectral matching accuracies within +/- 10.5% and +/- 9.3% for AM0G and AM1.5G, respectively, meeting the A+ level simulation standard for solar spectrum reconstruction considering AM0G and AM1.5G. These findings provide a theoretical foundation and technical advancements for high-precision solar spectrum reconstruction, which has practical implications for improving the efficiency and accuracy of solar energy systems, as well as supporting further research on solar spectrum utilization, and is expected to influence the development of more efficient solar simulators.

关键词： LED solar simulator multi-objective genetic algorithm neural network solar spectrum spectral simulation

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Joint optimization of energy consumption and latency for task offloading in cloud-edge collaboration system

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Jiang, Xue Dou, Haie Wang, Lei Xia, Zhijie Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr Commun & Network Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Nanjing Peoples R China

Ina cloud-edge collaboration system, ensuring consistent wireless connectivity that meets the quality of service (QoS) for users with varying requirements is challenging. Therefore, the approach to task offloading for all mobile devices (MDs) should be carefully designed to enhance performance based on different requirements. This paper introduces a novel multi-objective genetic algorithm (MOGA) to address the task offloading problem, considering minimal energy consumption and latency simultaneously. Experimental results demonstrate that the proposed multi-objective genetic algorithm achieves rapid convergence and optimizes delay and energy consumption.

关键词： Cloud-edge collaborative Task offloading multi-objective genetic algorithm

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Sustainable Water Optimization Tool (SUWO): An Optimization Framework for the Water-Energy-Food-Ecosystem Nexus

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WATER 2025年第9期17卷 1280-1280页

作者： Yaykiran, Salim Ekdal, Alpaslan Istanbul Tech Univ Environm Engn Dept TR-34469 Maslak Turkiye

Sustainable water management requires integrated approaches balancing competing demands and environmental sustainability. This study introduces the Sustainable Water Optimization Tool (SUWO), an open-source, Python-based simulation-optimization framework for basin-scale surface-water-resources management. SUWO employs the water-energy-food-ecosystem (WEF-E) nexus approach, utilizing a multi-objective genetic algorithm (MOGA) to generate Pareto-optimal solutions and facilitate a trade-off analysis among water uses through simulations of reservoir operations, hydro-energy production, irrigation, and flow regulation. SUWO integrates scenario analysis with multi-criteria decision making (MCDM), enabling the evaluation of various management, climate, and environmental scenarios. The framework was applied to the Sakarya River Basin (SRB) in T & uuml;rkiye, a rapidly developing region pressured by water infrastructure development, hydroelectric power plants (HEPPs), and irrigation expansion. The SUWO-SRB model showed that while Non-dominated Sorting genetic algorithm II (NSGA-II) generally exhibited superior performance, NSGA-III presented a competitive alternative. The optimization results were analyzed across four management scenarios under varying hydrological conditions and environmental management classes (EMCs) for the near future. The model results highlight WEF-E nexus trade-offs. Maximizing energy production often impacts irrigation and the ecosystem, while prioritizing sustainable irrigation can reduce energy output. Dry conditions reduce hydropower and irrigation capacity, emphasizing water scarcity vulnerabilities. Ecological deviation negatively correlates with anthropogenic factors.

关键词： water resources management multi-objective optimization multi-objective genetic algorithm WEF-E nexus trade-off analysis environmental flows reservoir operation irrigation Sakarya Basin Python

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Efficient risk-based inspection framework: Balancing safety and budgetary constraints

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 253卷

作者： Javid, Y. Kharazmi Univ Engn Fac Ind Engn Dept Tehran Iran

Efficient equipment maintenance is paramount across various industries to mitigate energy wastage and avert potential disasters such as hazardous emissions, fires, and explosions. Within this context, the adoption of risk- based inspection strategies has emerged as a crucial method for assessing equipment integrity. This study integrates the principles of Risk-Based Inspection (RBI) with a novel two-objective mathematical model, resulting in a comprehensive framework for equipment inspection programs. The primary aim of this framework is to reduce overall risk exposure while optimizing inspection expenditures. Unlike conventional approaches, this methodology eliminates the necessity to define threshold risk levels. By integrating inspection costs into the model, the assessment of Failure Consequences, and thereby, the decision-making process has been streamlined. This innovative algorithm effectively balances the reduction of failure likelihood with the minimization of inspection costs, enhancing decision-making capabilities. Importantly, this approach offers significant protection against energy wastage and the occurrence of leaks through robust risk management strategies. The algorithm employs specialized operators to expedite the discovery of optimal solutions. Empirical validation through a case study conducted at a Petrochemical Plant highlights the practicality and effectiveness of the proposed framework.

关键词： Inspection program Risk-based inspection multi-objective genetic algorithm Risk reduction

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