NSTL国家科技图书文献中心

1. Experimental and numerical simulation of expanded polystyrene pyrolysis coupled with thermal shrinkage and melting processes NSTL国家科技图书文献中心

Juan Zhang | Fahang Liu ... - 《Energy》 - 2025,314(Jan.1) - 134311.1～134311.11 - 共11页

摘要： Complex Evolution (SCE) algorithm based on TG data were | . The kinetic parameters optimized by the Shuffled | . The model could also predict complex* thermal effects* | , conversion and temperature evolution. Furthermore | This work developed a pyrolysis model for

关键词： EPS | Pyrolysis | Thermal shrinkage | Melt | Computational modeling | Uncertainty quantification

2. Novel evolutionary?optimized neural network for predicting landslide susceptibility NSTL国家科技图书文献中心

Rana Muhammad Adnan ... | Imran Khan ... - 《Environment, development and sustainability》 - 2024,26(7) - 17687～17719 - 共33页 - 被引量：15

摘要： explored in this paper are the SCE* and VSA algorithmsused* | for SCE-MLP and from 0.744 to 0.818 for VSA-MLP | , 100 to and 250.Also, in case of SCE-MLP, the | conditions of the SCE-MLPand VSA-MLP model showed 150 and | In order to mitigate/prevent the risks of

关键词： Neural metaheuristic algorithms | Landslide susceptibility | Optimization algorithms | Shuffled complex evolution (SCE) | Vortex search algorithm (VSA)

3. Shuffled multi-evolutionary algorithm with linear population size reduction NSTL国家科技图书文献中心

Morteza Alinia,Ahand... | Hamed,Kharrati ... - 《Soft computing》 - 2024,28(21/22) - 12671～12688 - 共18页

摘要： complex evolution (SCE), shuffled differential evolution | can be useful for finding global optima in complex | inspired from shuffled frog leaping (SFL), shuffled | (SCE) and opposition-based learning (OBL) are | incorporated into a new structure called the shuffled* multi*

关键词： Shuffling | Search moves | Evolutionary process | Population size reduction

4. Pile bearing capacity prediction in cold regions using a combination of ANN with metaheuristic algorithms NSTL国家科技图书文献中心

Zhou, Jingting | Moayedi, Hossein ... - 《Steel & Composite Structures: An International Journal》 - 2024,51(4) - 417～440 - 共24页

摘要：), league championship algorithm (LCA), shuffled complex | evolution (SCE), and symbiotic organisms search (SOS) to | , and the BHA, LCA, SCE, and SOS -based ANN models | determine the optimal BHA, LCA, SCE, and SOS parameters | forecasted by SCE-MLP. The testing dataset's respective

关键词： artificial neural network | bearing capacity | metaheuristic algorithms | pile | LATERALLY LOADED PILES | ARTIFICIAL NEURAL-NETWORK | DRIVEN PILES | FROZEN | MODEL | FOUNDATIONS...

5. Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problems NSTL国家科技图书文献中心

Mehta, Shikha - 《Journal of combinatorial optimization》 - 2024,47(2) - ARTN 6～ - 共38页 - 被引量：1

摘要： complex evolution (SCE) partitioning technique; Seed and | Shuffled Frog leaping algorithm (SFLA) is a | in determining its ability to solve complex | proposed SCP, AGP and WHP methods outperform Shuffled | multi population swarm intelligence algorithm which

关键词： Population partitioning algorithms | Multi population methods | SFLA | Swarm intelligence | Computationally expensive problems | CEC2015

6. Novel neural network-based metaheuristic models for the stability prediction of rectangular trapdoors in anisotropic and non-homogeneous clay NSTL国家科技图书文献中心

Kongtawan Sangjinda | Divesh Ranjan Kumar ... - 《Advances in engineering software》 - 2024,193(Jul.) - 103668.1～103668.24 - 共24页

摘要：), and shuffled* complex evolution (SCE), are also* | . The proposed ANN-SCE* model outperforms the other* | the ANN-SCE* model achieved the highest rank, with a* | The problem of trapdoor stability is a crucial | problem in geotechnical engineering. This study is the

关键词： Artificial neural network | Optimization algorithms | Stability | Trapdoor | FELA

7. An Automated Framework to Segment and Classify Gliomas using Hybrid Shuffled Complex Evolution with Convolutional Neural Network NSTL国家科技图书文献中心

G Valarmathy | N Umapathi ... - 《Przeglad Elektrotechniczny》 - 2024,100(7) - 158～165 - 共8页

摘要： Colony Optimization (ACO), hybrid Shuffled Complex | Evolution (SCE) with ACO and hybrid SCE with Particle | The infiltrative nature and rapid progression | of gliomas have made them the most prevalent as | well as aggressive type of brain tumour. In the

关键词： Glioma Detection and Segmentation | Magnetic Resonance Imaging (MRI) | Gabor Filter | Ant Colony Optimization (ACO) | Shuffle Complex Evolution (SCE) | Particle Swarm Optimization and Convolutional Neural Network

8. Kinetic, gas emission, reaction pathway and interaction mechanism analysis during oxidative pyrolysis of aluminium electrolysis waste with beech wood NSTL国家科技图书文献中心

Zhang J. | Chen W.... - 《Journal of analytical & applied pyrolysis》 - 2024,181(Aug.) - 1.1～1.9 - 共9页

摘要： Shuffled Complex Evolution (SCE) method, which were used | ? 2024 Elsevier B.V.The oxidative pyrolysis | kinetic, gas emission, reaction pathway and interaction | mechanism during oxidative pyrolysis of spent cathode | carbon block (SCCB) and beech wood (BW) were

关键词： Beech wood | Cathode carbon block waste | Gas emission | Interaction mechanism | Oxidative pyrolysis | Reaction pathway

9. State-of-the-art advanced hybrid ANNs paradigm for assessment and prediction of slope stability NSTL国家科技图书文献中心

Kumar Nitish | Kumari Sunita - 《Multiscale and multidisciplinary modeling, experiments and design》 - 2024,7(4) - 3483～3509 - 共27页

摘要： Firefly (FF), Ant Lion (ALO), shuffled complex evolution | (SCE), and the imperialist colony algorithm (ICA | Abstract The study presents advanced hybrid | artificial neural network (ANN) models to enhance the | analysis of slope stability and the prediction of the

关键词： Slope stability | Machine learning | Factor of safety | Artificial neural network | Optimization algorithm

10. In-depth exploration of waste printed circuit boards pyrolysis: Real-time products characteristics, kinetics parameter optimization and reaction mechanism NSTL国家科技图书文献中心

Na Du | Yuhui Li ... - 《Energy》 - 2024,312(Dec.15) - 133494.1～133494.15 - 共15页

摘要：) pyrolysis is developed by the shuffled complex evolution | (SCE) algorithm. Results indicate that the main | Pyrolysis is a promising technology for | recycling waste printed circuit boards (WPCBs). This study | delves into a real-time examination of the volatiles

摘要： Complex Evolution (SCE) algorithm based on TG data were | . The kinetic parameters optimized by the Shuffled | . The model could also predict complex* thermal effects* | , conversion and temperature evolution. Furthermore | This work developed a pyrolysis model for

关键词： EPS | Pyrolysis | Thermal shrinkage | Melt | Computational modeling | Uncertainty quantification

摘要： explored in this paper are the SCE* and VSA algorithmsused* | for SCE-MLP and from 0.744 to 0.818 for VSA-MLP | , 100 to and 250.Also, in case of SCE-MLP, the | conditions of the SCE-MLPand VSA-MLP model showed 150 and | In order to mitigate/prevent the risks of

关键词： Neural metaheuristic algorithms | Landslide susceptibility | Optimization algorithms | Shuffled complex evolution (SCE) | Vortex search algorithm (VSA)

摘要： complex evolution (SCE), shuffled differential evolution | can be useful for finding global optima in complex | inspired from shuffled frog leaping (SFL), shuffled | (SCE) and opposition-based learning (OBL) are | incorporated into a new structure called the shuffled* multi*

关键词： Shuffling | Search moves | Evolutionary process | Population size reduction

摘要：), league championship algorithm (LCA), shuffled complex | evolution (SCE), and symbiotic organisms search (SOS) to | , and the BHA, LCA, SCE, and SOS -based ANN models | determine the optimal BHA, LCA, SCE, and SOS parameters | forecasted by SCE-MLP. The testing dataset's respective

关键词： artificial neural network | bearing capacity | metaheuristic algorithms | pile | LATERALLY LOADED PILES | ARTIFICIAL NEURAL-NETWORK | DRIVEN PILES | FROZEN | MODEL | FOUNDATIONS...

摘要： complex evolution (SCE) partitioning technique; Seed and | Shuffled Frog leaping algorithm (SFLA) is a | in determining its ability to solve complex | proposed SCP, AGP and WHP methods outperform Shuffled | multi population swarm intelligence algorithm which

关键词： Population partitioning algorithms | Multi population methods | SFLA | Swarm intelligence | Computationally expensive problems | CEC2015

摘要：), and shuffled* complex evolution (SCE), are also* | . The proposed ANN-SCE* model outperforms the other* | the ANN-SCE* model achieved the highest rank, with a* | The problem of trapdoor stability is a crucial | problem in geotechnical engineering. This study is the

关键词： Artificial neural network | Optimization algorithms | Stability | Trapdoor | FELA

摘要： Colony Optimization (ACO), hybrid Shuffled Complex | Evolution (SCE) with ACO and hybrid SCE with Particle | The infiltrative nature and rapid progression | of gliomas have made them the most prevalent as | well as aggressive type of brain tumour. In the

关键词： Glioma Detection and Segmentation | Magnetic Resonance Imaging (MRI) | Gabor Filter | Ant Colony Optimization (ACO) | Shuffle Complex Evolution (SCE) | Particle Swarm Optimization and Convolutional Neural Network

摘要： Shuffled Complex Evolution (SCE) method, which were used | ? 2024 Elsevier B.V.The oxidative pyrolysis | kinetic, gas emission, reaction pathway and interaction | mechanism during oxidative pyrolysis of spent cathode | carbon block (SCCB) and beech wood (BW) were

关键词： Beech wood | Cathode carbon block waste | Gas emission | Interaction mechanism | Oxidative pyrolysis | Reaction pathway

摘要： Firefly (FF), Ant Lion (ALO), shuffled complex evolution | (SCE), and the imperialist colony algorithm (ICA | Abstract The study presents advanced hybrid | artificial neural network (ANN) models to enhance the | analysis of slope stability and the prediction of the

关键词： Slope stability | Machine learning | Factor of safety | Artificial neural network | Optimization algorithm

摘要：) pyrolysis is developed by the shuffled complex evolution | (SCE) algorithm. Results indicate that the main | Pyrolysis is a promising technology for | recycling waste printed circuit boards (WPCBs). This study | delves into a real-time examination of the volatiles

关键词： WPCBs | Pyrolysis | Real-time product analysis | Kinetics parameter optimization | Pyrolysis mechanism

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Complex Evolution (SCE) algorithm based on TG data were | . The kinetic parameters optimized by the Shuffled | . The model could also predict complex thermal effects | , conversion and temperature evolution. Furthermore | This work developed a pyrolysis model for

关键词： EPS | Pyrolysis | Thermal shrinkage | Melt | Computational modeling | Uncertainty quantification

摘要： explored in this paper are the SCE and VSA algorithmsused | for SCE-MLP and from 0.744 to 0.818 for VSA-MLP | , 100 to and 250.Also, in case of SCE-MLP, the | conditions of the SCE-MLPand VSA-MLP model showed 150 and | In order to mitigate/prevent the risks of

关键词： Neural metaheuristic algorithms | Landslide susceptibility | Optimization algorithms | Shuffled complex evolution (SCE) | Vortex search algorithm (VSA)

摘要： complex evolution (SCE), shuffled differential evolution | can be useful for finding global optima in complex | inspired from shuffled frog leaping (SFL), shuffled | (SCE) and opposition-based learning (OBL) are | incorporated into a new structure called the shuffled multi

关键词： Shuffling | Search moves | Evolutionary process | Population size reduction

摘要：), league championship algorithm (LCA), shuffled complex | evolution (SCE), and symbiotic organisms search (SOS) to | , and the BHA, LCA, SCE, and SOS -based ANN models | determine the optimal BHA, LCA, SCE, and SOS parameters | forecasted by SCE-MLP. The testing dataset's respective

关键词： artificial neural network | bearing capacity | metaheuristic algorithms | pile | LATERALLY LOADED PILES | ARTIFICIAL NEURAL-NETWORK | DRIVEN PILES | FROZEN | MODEL | FOUNDATIONS...

摘要： complex evolution (SCE) partitioning technique; Seed and | Shuffled Frog leaping algorithm (SFLA) is a | in determining its ability to solve complex | proposed SCP, AGP and WHP methods outperform Shuffled | multi population swarm intelligence algorithm which

关键词： Population partitioning algorithms | Multi population methods | SFLA | Swarm intelligence | Computationally expensive problems | CEC2015

摘要：), and shuffled complex evolution (SCE), are also | . The proposed ANN-SCE model outperforms the other | the ANN-SCE model achieved the highest rank, with a | The problem of trapdoor stability is a crucial | problem in geotechnical engineering. This study is the

关键词： Artificial neural network | Optimization algorithms | Stability | Trapdoor | FELA

摘要： Colony Optimization (ACO), hybrid Shuffled Complex | Evolution (SCE) with ACO and hybrid SCE with Particle | The infiltrative nature and rapid progression | of gliomas have made them the most prevalent as | well as aggressive type of brain tumour. In the

关键词： Glioma Detection and Segmentation | Magnetic Resonance Imaging (MRI) | Gabor Filter | Ant Colony Optimization (ACO) | Shuffle Complex Evolution (SCE) | Particle Swarm Optimization and Convolutional Neural Network

摘要： Shuffled Complex Evolution (SCE) method, which were used | ? 2024 Elsevier B.V.The oxidative pyrolysis | kinetic, gas emission, reaction pathway and interaction | mechanism during oxidative pyrolysis of spent cathode | carbon block (SCCB) and beech wood (BW) were

关键词： Beech wood | Cathode carbon block waste | Gas emission | Interaction mechanism | Oxidative pyrolysis | Reaction pathway

摘要： Firefly (FF), Ant Lion (ALO), shuffled complex evolution | (SCE), and the imperialist colony algorithm (ICA | Abstract The study presents advanced hybrid | artificial neural network (ANN) models to enhance the | analysis of slope stability and the prediction of the

关键词： Slope stability | Machine learning | Factor of safety | Artificial neural network | Optimization algorithm

摘要：) pyrolysis is developed by the shuffled complex evolution | (SCE) algorithm. Results indicate that the main | Pyrolysis is a promising technology for | recycling waste printed circuit boards (WPCBs). This study | delves into a real-time examination of the volatiles

关键词： WPCBs | Pyrolysis | Real-time product analysis | Kinetics parameter optimization | Pyrolysis mechanism

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Complex Evolution (SCE) algorithm based on TG data were | . The kinetic parameters optimized by the Shuffled | . The model could also predict complex* thermal effects* | , conversion and temperature evolution. Furthermore | This work developed a pyrolysis model for

摘要： explored in this paper are the SCE* and VSA algorithmsused* | for SCE-MLP and from 0.744 to 0.818 for VSA-MLP | , 100 to and 250.Also, in case of SCE-MLP, the | conditions of the SCE-MLPand VSA-MLP model showed 150 and | In order to mitigate/prevent the risks of

摘要： complex evolution (SCE), shuffled differential evolution | can be useful for finding global optima in complex | inspired from shuffled frog leaping (SFL), shuffled | (SCE) and opposition-based learning (OBL) are | incorporated into a new structure called the shuffled* multi*

摘要：), and shuffled* complex evolution (SCE), are also* | . The proposed ANN-SCE* model outperforms the other* | the ANN-SCE* model achieved the highest rank, with a* | The problem of trapdoor stability is a crucial | problem in geotechnical engineering. This study is the