NSTL国家科技图书文献中心

1. Design of Automated Container Layout and Resource Optimization Algorithm based on Cloud Computing Technology NSTL国家科技图书文献中心

Feng Zhang - 《International Conference on Mechatronics and Intelligent Control (ICMIC 2024),Part One of Two Parts》 - International Conference on Mechatronics and Intelligent Control - 2025, - 1344708.1～1344708.6 - 共6页

摘要： automated container arrangement and resource optimization | experimental results show that our optimization* algorithm has* | algorithm design based on cloud computing technology to | address these issues. This algorithm* can predict and* | of applications, ensuring efficient resource

关键词： Cloud computing technology | Container arrangement | Resource optimization algorithm | Experiment and verification

2. A resource allocation algorithm based on hybrid spider wasp optimization for cognitive radio networks NSTL国家科技图书文献中心

Shuo Shang | Mingyue Zhou - 《Physical communication》 - 2025,70(Jun.) - 102625.1～102625.11 - 共11页

摘要： Optimization (SWO) algorithm to solve this optimization | Optimization algorithm (HSWO) is proposed. This algorithm | traditional resource* allocation algorithms are difficult to* | focusing on intelligent optimization* algorithms. Our* | the flexibility of the algorithm, we transform the

关键词： Cognitive radio | Intelligent optimization algorithms | Spider Wasp Optimization algorithm | Flexibility

3. An Advanced Energy Efficient Resource Allocation for Software-Defined WSN Using Hybrid Optimization Algorithm NSTL国家科技图书文献中心

Ramachandra Ballary | Rajeshwari M. Hegde - 《International journal of communication systems》 - 2025,38(3) - e6111.1～e6111.20 - 共20页

摘要： Election-Based Optimization* Algorithm (EBOA) with the* | Ladybird Beetle Optimization* Algorithm (LBOA), and the* | by incorporating optimization* techniques. A novel* | hybrid optimization* strategy is created by merging the* | Beetle Optimization* (HELBO). This work examines an*

关键词： energy efficient resource allocation | hybrid election-based ladybug beetle optimization | power consumption minimization | software-defined wireless sensor networking

4. Joint optimization scheme for task offloading and resource allocation based on MO-MFEA algorithm in intelligent transportation scenarios NSTL国家科技图书文献中心

Zhao M. | Liu C.... - 《Journal of network and computer applications》 - 2025,233(Jan.) - 1.1～1.13 - 共13页

摘要：-objective optimization* algorithm (MO-MFEA-S) with adaptive* | computation offloading and resource* allocation adopting edge* | , computation resource* allocation and transmission power* | © 2024 Elsevier LtdWith the surge of | transportation data and diversification of services, the

关键词： Computation offloading | Content caching | edge computing | Intelligent transportation system | Non-orthogonal multiple access | Resource allocation | Structured tasks

5. Variable-coupled distributed resource allocations for multi-agent system with limited admissible interaction NSTL国家科技图书文献中心

Luan, Linhua | Qin, Sitian - 《ISA Transactions》 - 2025,157 - 35～45 - 共11页

摘要： distributed optimization* algorithm to address the VCDRA in* | distributed optimization* algorithm improves existing* | proposed distributed optimization* algorithm no longer* | Variable-coupled distributed resource | allocations (VCDRAs) involve optimizing resource

关键词： Variable-coupled distributed resource allocation | Multi-agent system | Distributed optimization algorithm | Admissible interaction range | External disturbances

6. Multiobjective Ant Colony System Algorithm for Component-Level Construction Schedule Optimization NSTL国家科技图书文献中心

Zhaozheng Shen | Jie Wu - 《Journal of construction engineering and management》 - 2025,151(3) - 1.1～1.14 - 共14页

摘要： study formulates an optimization* model for the* | component-level resource-constrained project scheduling | system (MOACS) algorithm* is developed that utilizes* | of other competitive multiobjective optimization | Abstract Automatic generation of construction

关键词： Component-level | Resource-constrained project scheduling problem | Ant colony system algorithm | Multiobjective optimization | Carbon emission

7. Resource-efficient Algorithms and Systems of Foundation Models: A Survey NSTL国家科技图书文献中心

MENGWEI XU | DONGQI CAI ... - 《ACM computing surveys》 - 2025,57(5) - 110.1～110.39 - 共39页

摘要： resource-efficient strategies. This survey delves into | tackling the resource* challenges posed by large* | Large foundationmodels, including large | languagemodels, vision transformers, diffusion, and large | language model based multimodal models, are

关键词： Resource efficiency | foundation models | algorithm and system optimization

8. A Distributed Mobile Edge Computing Based Dynamic Resource Allocation in 5G Network Using Green Anaconda Optimization Based Deep Learning Network NSTL国家科技图书文献中心

Selvan C | G. Govinda Rajulu ... - 《International journal of communication systems》 - 2025,38(5) - e6050.1～e6050.17 - 共17页

摘要： optimization (GAO) algorithm is used to optimize the | resource allocation (RA), energy efficiency (EE), and | Mobile edge computing (MEC) facilitates | storage, cloud computing, and analysis capabilities near | to the users in 5G communication systems. MEC and

关键词： 5G networks | green anaconda optimization (GAO) algorithm | hybrid quantum-classical convolutional neural network (HQCCNN) | mobile edge computing (MEC) | resource allocation (RA) and energy efficiency (EE) | simplicial attention network (SAN)

9. Optimization of power system network topology reconstruction algorithm based on edge computing NSTL国家科技图书文献中心

Yuankun Jiang | Zenghai Wang ... - 《International Conference on Physics,Photonics,and Optical Engineering (ICPPOE 2024),Part One of Two Parts》 - International Conference on Physics,Photonics,and Optical Engineering - 2025, - 135520M.1～135520M.9 - 共9页

摘要： of data set diversity, algorithm* optimization and* | system network topology reconstruction algorithm* using* | topology. This paper uses genetic algorithm* for multi* | -objective optimization, taking into account processing | efficiency, resource* allocation and fault response*

关键词： Edge computing | Power system | Network topology reconstruction | Algorithm optimization | Genetic algorithm

10. An energy-aware migration framework using metaheuristic algorithm in cloud computing NSTL国家科技图书文献中心

Singhal, Saurabh | Sharma, Ashish - 《Knowledge and information systems》 - 2025,67(2) - 1373～1398 - 共26页

摘要： metaheuristic Rock Hyrax algorithm-based cloud VM migration | proposed algorithm* for finding energy-efficient compute* | optimizing resource* utilization. When evaluating the* | were considered. The proposed algorithm* is simulated* | strategy outperforms ant colony optimization, particle

摘要： automated container arrangement and resource optimization | experimental results show that our optimization* algorithm has* | algorithm design based on cloud computing technology to | address these issues. This algorithm* can predict and* | of applications, ensuring efficient resource

关键词： Cloud computing technology | Container arrangement | Resource optimization algorithm | Experiment and verification

摘要： Optimization (SWO) algorithm to solve this optimization | Optimization algorithm (HSWO) is proposed. This algorithm | traditional resource* allocation algorithms are difficult to* | focusing on intelligent optimization* algorithms. Our* | the flexibility of the algorithm, we transform the

关键词： Cognitive radio | Intelligent optimization algorithms | Spider Wasp Optimization algorithm | Flexibility

摘要： Election-Based Optimization* Algorithm (EBOA) with the* | Ladybird Beetle Optimization* Algorithm (LBOA), and the* | by incorporating optimization* techniques. A novel* | hybrid optimization* strategy is created by merging the* | Beetle Optimization* (HELBO). This work examines an*

关键词： energy efficient resource allocation | hybrid election-based ladybug beetle optimization | power consumption minimization | software-defined wireless sensor networking

摘要：-objective optimization* algorithm (MO-MFEA-S) with adaptive* | computation offloading and resource* allocation adopting edge* | , computation resource* allocation and transmission power* | © 2024 Elsevier LtdWith the surge of | transportation data and diversification of services, the

关键词： Computation offloading | Content caching | edge computing | Intelligent transportation system | Non-orthogonal multiple access | Resource allocation | Structured tasks

摘要： distributed optimization* algorithm to address the VCDRA in* | distributed optimization* algorithm improves existing* | proposed distributed optimization* algorithm no longer* | Variable-coupled distributed resource | allocations (VCDRAs) involve optimizing resource

关键词： Variable-coupled distributed resource allocation | Multi-agent system | Distributed optimization algorithm | Admissible interaction range | External disturbances

摘要： study formulates an optimization* model for the* | component-level resource-constrained project scheduling | system (MOACS) algorithm* is developed that utilizes* | of other competitive multiobjective optimization | Abstract Automatic generation of construction

关键词： Component-level | Resource-constrained project scheduling problem | Ant colony system algorithm | Multiobjective optimization | Carbon emission

摘要： resource-efficient strategies. This survey delves into | tackling the resource* challenges posed by large* | Large foundationmodels, including large | languagemodels, vision transformers, diffusion, and large | language model based multimodal models, are

关键词： Resource efficiency | foundation models | algorithm and system optimization

摘要： optimization (GAO) algorithm is used to optimize the | resource allocation (RA), energy efficiency (EE), and | Mobile edge computing (MEC) facilitates | storage, cloud computing, and analysis capabilities near | to the users in 5G communication systems. MEC and

关键词： 5G networks | green anaconda optimization (GAO) algorithm | hybrid quantum-classical convolutional neural network (HQCCNN) | mobile edge computing (MEC) | resource allocation (RA) and energy efficiency (EE) | simplicial attention network (SAN)

摘要： of data set diversity, algorithm* optimization and* | system network topology reconstruction algorithm* using* | topology. This paper uses genetic algorithm* for multi* | -objective optimization, taking into account processing | efficiency, resource* allocation and fault response*

关键词： Edge computing | Power system | Network topology reconstruction | Algorithm optimization | Genetic algorithm

摘要： metaheuristic Rock Hyrax algorithm-based cloud VM migration | proposed algorithm* for finding energy-efficient compute* | optimizing resource* utilization. When evaluating the* | were considered. The proposed algorithm* is simulated* | strategy outperforms ant colony optimization, particle

关键词： Cloud computing | Energy consumption | Makespan time | Resource utilization | Rock Hyrax optimization | SLA violations | VM migration

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： automated container arrangement and resource optimization | experimental results show that our optimization algorithm has | algorithm design based on cloud computing technology to | address these issues. This algorithm can predict and | of applications, ensuring efficient resource

关键词： Cloud computing technology | Container arrangement | Resource optimization algorithm | Experiment and verification

摘要： Optimization (SWO) algorithm to solve this optimization | Optimization algorithm (HSWO) is proposed. This algorithm | traditional resource allocation algorithms are difficult to | focusing on intelligent optimization algorithms. Our | the flexibility of the algorithm, we transform the

关键词： Cognitive radio | Intelligent optimization algorithms | Spider Wasp Optimization algorithm | Flexibility

摘要： Election-Based Optimization Algorithm (EBOA) with the | Ladybird Beetle Optimization Algorithm (LBOA), and the | by incorporating optimization techniques. A novel | hybrid optimization strategy is created by merging the | Beetle Optimization (HELBO). This work examines an

关键词： energy efficient resource allocation | hybrid election-based ladybug beetle optimization | power consumption minimization | software-defined wireless sensor networking

摘要：-objective optimization algorithm (MO-MFEA-S) with adaptive | computation offloading and resource allocation adopting edge | , computation resource allocation and transmission power | © 2024 Elsevier LtdWith the surge of | transportation data and diversification of services, the

关键词： Computation offloading | Content caching | edge computing | Intelligent transportation system | Non-orthogonal multiple access | Resource allocation | Structured tasks

摘要： distributed optimization algorithm to address the VCDRA in | distributed optimization algorithm improves existing | proposed distributed optimization algorithm no longer | Variable-coupled distributed resource | allocations (VCDRAs) involve optimizing resource

关键词： Variable-coupled distributed resource allocation | Multi-agent system | Distributed optimization algorithm | Admissible interaction range | External disturbances

摘要： study formulates an optimization model for the | component-level resource-constrained project scheduling | system (MOACS) algorithm is developed that utilizes | of other competitive multiobjective optimization | Abstract Automatic generation of construction

关键词： Component-level | Resource-constrained project scheduling problem | Ant colony system algorithm | Multiobjective optimization | Carbon emission

摘要： resource-efficient strategies. This survey delves into | tackling the resource challenges posed by large | Large foundationmodels, including large | languagemodels, vision transformers, diffusion, and large | language model based multimodal models, are

关键词： Resource efficiency | foundation models | algorithm and system optimization

摘要： optimization (GAO) algorithm is used to optimize the | resource allocation (RA), energy efficiency (EE), and | Mobile edge computing (MEC) facilitates | storage, cloud computing, and analysis capabilities near | to the users in 5G communication systems. MEC and

关键词： 5G networks | green anaconda optimization (GAO) algorithm | hybrid quantum-classical convolutional neural network (HQCCNN) | mobile edge computing (MEC) | resource allocation (RA) and energy efficiency (EE) | simplicial attention network (SAN)

摘要： of data set diversity, algorithm optimization and | system network topology reconstruction algorithm using | topology. This paper uses genetic algorithm for multi | -objective optimization, taking into account processing | efficiency, resource allocation and fault response

关键词： Edge computing | Power system | Network topology reconstruction | Algorithm optimization | Genetic algorithm

摘要： metaheuristic Rock Hyrax algorithm-based cloud VM migration | proposed algorithm for finding energy-efficient compute | optimizing resource utilization. When evaluating the | were considered. The proposed algorithm is simulated | strategy outperforms ant colony optimization, particle

关键词： Cloud computing | Energy consumption | Makespan time | Resource utilization | Rock Hyrax optimization | SLA violations | VM migration

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： automated container arrangement and resource optimization | experimental results show that our optimization* algorithm has* | algorithm design based on cloud computing technology to | address these issues. This algorithm* can predict and* | of applications, ensuring efficient resource

摘要： Optimization (SWO) algorithm to solve this optimization | Optimization algorithm (HSWO) is proposed. This algorithm | traditional resource* allocation algorithms are difficult to* | focusing on intelligent optimization* algorithms. Our* | the flexibility of the algorithm, we transform the

摘要： Election-Based Optimization* Algorithm (EBOA) with the* | Ladybird Beetle Optimization* Algorithm (LBOA), and the* | by incorporating optimization* techniques. A novel* | hybrid optimization* strategy is created by merging the* | Beetle Optimization* (HELBO). This work examines an*

摘要：-objective optimization* algorithm (MO-MFEA-S) with adaptive* | computation offloading and resource* allocation adopting edge* | , computation resource* allocation and transmission power* | © 2024 Elsevier LtdWith the surge of | transportation data and diversification of services, the

摘要： distributed optimization* algorithm to address the VCDRA in* | distributed optimization* algorithm improves existing* | proposed distributed optimization* algorithm no longer* | Variable-coupled distributed resource | allocations (VCDRAs) involve optimizing resource

摘要： study formulates an optimization* model for the* | component-level resource-constrained project scheduling | system (MOACS) algorithm* is developed that utilizes* | of other competitive multiobjective optimization | Abstract Automatic generation of construction

摘要： resource-efficient strategies. This survey delves into | tackling the resource* challenges posed by large* | Large foundationmodels, including large | languagemodels, vision transformers, diffusion, and large | language model based multimodal models, are

摘要： of data set diversity, algorithm* optimization and* | system network topology reconstruction algorithm* using* | topology. This paper uses genetic algorithm* for multi* | -objective optimization, taking into account processing | efficiency, resource* allocation and fault response*