NSTL国家科技图书文献中心

1. Minimum Adjustment Consensus Optimization Models With Fuzzy Preference Relations: The Perspective of Cardinal and Ordinal Consensus NSTL国家科技图书文献中心

Zhengmin Liu | Wenxin Wang ... - 《IEEE Transactions on Fuzzy Systems》 - 2025,33(2) - 680～693 - 共14页

摘要： optimization models that strive to minimize adjustments to | consensus models* have primarily focused on cardinal* | two mixed-integer programming models* aimed at* | minimizing preference adjustments. The proposed models* have* | In group decision-making (GDM), traditional

关键词： Biological system modeling | Optimization models | Consensus control | Optimization | Fuzzy systems | Economics | Rendering (computer graphics) | Position measurement | Object recognition | Numerical models

2. Cost efficiency in water supply systems: An applied review on optimization models for the pump scheduling problem NSTL国家科技图书文献中心 OA开放获取

Bras, Marlene | Moura, Ana ... - 《European Journal of Operational Research》 - 2025,323(1) - 1～19 - 共19页

摘要： established optimization* models for the PSP have not yet* | optimization. This optimization challenge is commonly | optimization problem, the large majority of the studies are | focus on the optimization* techniques, sidelining the* | addition to general pumps' operation optimization, the

关键词： Nonlinear programming | Linear programming | Water supply systems | Pump scheduling problem | Optimization models

3. ASP-DRL: A Novel Framework for Unifying IoT Energy Usage Flexibilities Characterized by Neural Networks and Optimization Models NSTL国家科技图书文献中心

Tao Qian | Mingyu Fang ... - 《IEEE internet of things journal》 - 2025,12(2) - 2251～2264 - 共14页

摘要： objective functions for optimization* models or to guide* | -carbon future. However, the various models* of these* | scheduling mechanisms for neural networks and optimization | models to maximize total social welfare. Within the ASP | optimization-based approaches.

关键词： Uncertainty | Neural networks | Internet of Things | Optimization models | Electric vehicle charging | Stochastic processes | Energy management | Microgrids | Renewable energy sources | Electricity

4. Multi-stage stochastic districting: optimization models and solution algorithms NSTL国家科技图书文献中心

Anika Pomes | Antonio Diglio ... - 《Annals of operations research》 - 2025,346(3) - 2225～2251 - 共27页

摘要：This paper investigates a Multi-Stage | Stochastic Districting Problem (MSSDP). The goal is to | devise a districting plan (i.e., clusters of | Territorial Units-TUs) accounting for uncertain parameters | changing over a discrete multi-period planning horizon

关键词： Districting | Multi-stage stochastic programming | Heuristics

5. On the sensitivity of some portfolio optimization models using interval analysis NSTL国家科技图书文献中心

Singh, Sarishti | Panda, Geetanjali - 《Opsearch》 - 2025,62(1) - 77～103 - 共27页

摘要： the portfolio optimization* models changes, and the* | portfolio is studied when the portfolio optimization* model* | classical approaches for solving these models* are not* | suitable. Here, we represent these models* with varying* | In this paper, the performance of the optimal

关键词： Portfolio selection | Sensitivity analysis | Interval analysis | Interval linear equations

6. Groundwater contaminant source identification using swarm intelligence-based simulation optimization models NSTL国家科技图书文献中心

K.,Swetha | T. I.,Eldho ... - 《Environmental Science and Pollution Research》 - 2025,32(3) - 1626～1639 - 共14页

摘要： different swarm intelligence-based optimization models | optimization (PSO) to form three SO models namely LRPIM-PSO | optimization (SO) model is presented for identification of | two steps namely, simulation and optimization. The | namely, teaching–learning based optimization* (TLBO*

关键词： Meshless method | Source identification | Local radial point interpolation method (LRPIM) | Optimization | PSO | GWO | TLBO

7. An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models NSTL国家科技图书文献中心

K.,Mukilan | C.,Rameshbabu ... - 《Neural computing & applications》 - 2025,37(1) - 169～184 - 共16页

摘要：, comparative analysis with other optimization models | Improved Monarch Butterfly Optimization* Algorithm (IMBOA* | models typically optimize only a single objective, such | Butterfly Optimization* Algorithm (MBOA) and its improved* | The Engineering Procurement Construction (EPC

关键词： EPC | Claim management system | Improved monarch butterfly optimization | Risk analysis | Cost | Quality

8. DPO: Discrete Prompt Optimization for Vision-Language Models NSTL国家科技图书文献中心

Nanhao Liang | Yong Liu - 《IEEE signal processing letters》 - 2025,32 - 671～675 - 共5页

摘要：-language models* (VLMs) has catalyzed the development of* | , we introduce Discrete Prompt Optimization* (DPO), a* | In recent years, the emergence of large vision | prompt learning, where networks are trained to enhance | VLM performance by learning continuous prompts

关键词： Training | Optimization | Adaptation models | Visualization | Overfitting | Vectors | Vocabulary | Signal processing algorithms | Stochastic processes | Standards

9. Artificial intelligence and machine learning models application in biodiesel optimization process and fuel properties prediction NSTL国家科技图书文献中心

Muhammad Arif | Adel I. Alalawy ... - 《Sustainable energy technologies and assessments》 - 2025,73(Jan.) - 104097.1～104097.20 - 共20页

摘要： optimization of transesterification parameters and fuel | feedstock selection, optimization, process monitoring, and | through ML, the efficiency of different models, and | %. ML models* demonstrated higher efficacy in* | transesterification optimization* to attain > 90 % yield. Various AI*

关键词： Biodiesel | Artificial intelligence | Machine learning models | Optimization | Predictions

10. A Python tool for parameter estimation of "black box" macro- and micro-kinetic models with Bayesian optimization - petBOA NSTL国家科技图书文献中心

Kasiraju, Sashank | Wang, Yifan ... - 《Computer physics communications》 - 2025,306 - 共14页

摘要： Parameter Estimation Tool utilizing Bayesian Optimization | kinetic models. We provide examples for Python | Optimization (BO) is implemented using the open-source | utilizes Bayesian Optimization* and offers a unique* | wrapper interface for expensive black-box kinetic models

摘要： optimization models that strive to minimize adjustments to | consensus models* have primarily focused on cardinal* | two mixed-integer programming models* aimed at* | minimizing preference adjustments. The proposed models* have* | In group decision-making (GDM), traditional

关键词： Biological system modeling | Optimization models | Consensus control | Optimization | Fuzzy systems | Economics | Rendering (computer graphics) | Position measurement | Object recognition | Numerical models

摘要： established optimization* models for the PSP have not yet* | optimization. This optimization challenge is commonly | optimization problem, the large majority of the studies are | focus on the optimization* techniques, sidelining the* | addition to general pumps' operation optimization, the

关键词： Nonlinear programming | Linear programming | Water supply systems | Pump scheduling problem | Optimization models

摘要： objective functions for optimization* models or to guide* | -carbon future. However, the various models* of these* | scheduling mechanisms for neural networks and optimization | models to maximize total social welfare. Within the ASP | optimization-based approaches.

关键词： Uncertainty | Neural networks | Internet of Things | Optimization models | Electric vehicle charging | Stochastic processes | Energy management | Microgrids | Renewable energy sources | Electricity

摘要：This paper investigates a Multi-Stage | Stochastic Districting Problem (MSSDP). The goal is to | devise a districting plan (i.e., clusters of | Territorial Units-TUs) accounting for uncertain parameters | changing over a discrete multi-period planning horizon

关键词： Districting | Multi-stage stochastic programming | Heuristics

摘要： the portfolio optimization* models changes, and the* | portfolio is studied when the portfolio optimization* model* | classical approaches for solving these models* are not* | suitable. Here, we represent these models* with varying* | In this paper, the performance of the optimal

关键词： Portfolio selection | Sensitivity analysis | Interval analysis | Interval linear equations

摘要： different swarm intelligence-based optimization models | optimization (PSO) to form three SO models namely LRPIM-PSO | optimization (SO) model is presented for identification of | two steps namely, simulation and optimization. The | namely, teaching–learning based optimization* (TLBO*

关键词： Meshless method | Source identification | Local radial point interpolation method (LRPIM) | Optimization | PSO | GWO | TLBO

摘要：, comparative analysis with other optimization models | Improved Monarch Butterfly Optimization* Algorithm (IMBOA* | models typically optimize only a single objective, such | Butterfly Optimization* Algorithm (MBOA) and its improved* | The Engineering Procurement Construction (EPC

关键词： EPC | Claim management system | Improved monarch butterfly optimization | Risk analysis | Cost | Quality

摘要：-language models* (VLMs) has catalyzed the development of* | , we introduce Discrete Prompt Optimization* (DPO), a* | In recent years, the emergence of large vision | prompt learning, where networks are trained to enhance | VLM performance by learning continuous prompts

关键词： Training | Optimization | Adaptation models | Visualization | Overfitting | Vectors | Vocabulary | Signal processing algorithms | Stochastic processes | Standards

关键词： Biodiesel | Artificial intelligence | Machine learning models | Optimization | Predictions

摘要： Parameter Estimation Tool utilizing Bayesian Optimization | kinetic models. We provide examples for Python | Optimization (BO) is implemented using the open-source | utilizes Bayesian Optimization* and offers a unique* | wrapper interface for expensive black-box kinetic models

关键词： Parameter estimation | Bayesian optimization | Gaussian process | Macrokinetic models | Microkinetic models

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： optimization models that strive to minimize adjustments to | consensus models have primarily focused on cardinal | two mixed-integer programming models aimed at | minimizing preference adjustments. The proposed models have | In group decision-making (GDM), traditional

关键词： Biological system modeling | Optimization models | Consensus control | Optimization | Fuzzy systems | Economics | Rendering (computer graphics) | Position measurement | Object recognition | Numerical models

摘要： established optimization models for the PSP have not yet | optimization. This optimization challenge is commonly | optimization problem, the large majority of the studies are | focus on the optimization techniques, sidelining the | addition to general pumps' operation optimization, the

关键词： Nonlinear programming | Linear programming | Water supply systems | Pump scheduling problem | Optimization models

摘要： objective functions for optimization models or to guide | -carbon future. However, the various models of these | scheduling mechanisms for neural networks and optimization | models to maximize total social welfare. Within the ASP | optimization-based approaches.

关键词： Uncertainty | Neural networks | Internet of Things | Optimization models | Electric vehicle charging | Stochastic processes | Energy management | Microgrids | Renewable energy sources | Electricity

摘要：This paper investigates a Multi-Stage | Stochastic Districting Problem (MSSDP). The goal is to | devise a districting plan (i.e., clusters of | Territorial Units-TUs) accounting for uncertain parameters | changing over a discrete multi-period planning horizon

关键词： Districting | Multi-stage stochastic programming | Heuristics

摘要： the portfolio optimization models changes, and the | portfolio is studied when the portfolio optimization model | classical approaches for solving these models are not | suitable. Here, we represent these models with varying | In this paper, the performance of the optimal

关键词： Portfolio selection | Sensitivity analysis | Interval analysis | Interval linear equations

摘要： different swarm intelligence-based optimization models | optimization (PSO) to form three SO models namely LRPIM-PSO | optimization (SO) model is presented for identification of | two steps namely, simulation and optimization. The | namely, teaching–learning based optimization (TLBO

关键词： Meshless method | Source identification | Local radial point interpolation method (LRPIM) | Optimization | PSO | GWO | TLBO

摘要：, comparative analysis with other optimization models | Improved Monarch Butterfly Optimization Algorithm (IMBOA | models typically optimize only a single objective, such | Butterfly Optimization Algorithm (MBOA) and its improved | The Engineering Procurement Construction (EPC

关键词： EPC | Claim management system | Improved monarch butterfly optimization | Risk analysis | Cost | Quality

摘要：-language models (VLMs) has catalyzed the development of | , we introduce Discrete Prompt Optimization (DPO), a | In recent years, the emergence of large vision | prompt learning, where networks are trained to enhance | VLM performance by learning continuous prompts

关键词： Training | Optimization | Adaptation models | Visualization | Overfitting | Vectors | Vocabulary | Signal processing algorithms | Stochastic processes | Standards

摘要： optimization of transesterification parameters and fuel | feedstock selection, optimization, process monitoring, and | through ML, the efficiency of different models, and | %. ML models demonstrated higher efficacy in | transesterification optimization to attain > 90 % yield. Various AI

关键词： Biodiesel | Artificial intelligence | Machine learning models | Optimization | Predictions

摘要： Parameter Estimation Tool utilizing Bayesian Optimization | kinetic models. We provide examples for Python | Optimization (BO) is implemented using the open-source | utilizes Bayesian Optimization and offers a unique | wrapper interface for expensive black-box kinetic models

关键词： Parameter estimation | Bayesian optimization | Gaussian process | Macrokinetic models | Microkinetic models

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： optimization models that strive to minimize adjustments to | consensus models* have primarily focused on cardinal* | two mixed-integer programming models* aimed at* | minimizing preference adjustments. The proposed models* have* | In group decision-making (GDM), traditional

摘要： objective functions for optimization* models or to guide* | -carbon future. However, the various models* of these* | scheduling mechanisms for neural networks and optimization | models to maximize total social welfare. Within the ASP | optimization-based approaches.

摘要： the portfolio optimization* models changes, and the* | portfolio is studied when the portfolio optimization* model* | classical approaches for solving these models* are not* | suitable. Here, we represent these models* with varying* | In this paper, the performance of the optimal

摘要： different swarm intelligence-based optimization models | optimization (PSO) to form three SO models namely LRPIM-PSO | optimization (SO) model is presented for identification of | two steps namely, simulation and optimization. The | namely, teaching–learning based optimization* (TLBO*

摘要：, comparative analysis with other optimization models | Improved Monarch Butterfly Optimization* Algorithm (IMBOA* | models typically optimize only a single objective, such | Butterfly Optimization* Algorithm (MBOA) and its improved* | The Engineering Procurement Construction (EPC

摘要：-language models* (VLMs) has catalyzed the development of* | , we introduce Discrete Prompt Optimization* (DPO), a* | In recent years, the emergence of large vision | prompt learning, where networks are trained to enhance | VLM performance by learning continuous prompts

摘要： Parameter Estimation Tool utilizing Bayesian Optimization | kinetic models. We provide examples for Python | Optimization (BO) is implemented using the open-source | utilizes Bayesian Optimization* and offers a unique* | wrapper interface for expensive black-box kinetic models