Gotowa bibliografia na temat „Nondominated sorting genetics algorithm (C-NSGA-II)”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Nondominated sorting genetics algorithm (C-NSGA-II)”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Nondominated sorting genetics algorithm (C-NSGA-II)"
Maximov, Jordan, Galya Duncheva, Angel Anchev, Vladimir Dunchev, Vladimir Todorov i Yaroslav Argirov. "Influence of an Ageing Heat Treatment on the Mechanical Characteristics of Iron-Aluminium Bronzes with β-Transformation Obtained via Centrifugal Casting: Modelling and Optimisation". Metals 13, nr 12 (24.11.2023): 1930. http://dx.doi.org/10.3390/met13121930.
Pełny tekst źródłaZhang, Weipeng, Ke Wang i Chang Chen. "Artificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries". Foods 11, nr 21 (25.10.2022): 3347. http://dx.doi.org/10.3390/foods11213347.
Pełny tekst źródłaGong, Guiliang, Qianwang Deng, Xuran Gong, Like Zhang, Haibin Wang i He Xie. "A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery". Mathematical Problems in Engineering 2018 (19.06.2018): 1–21. http://dx.doi.org/10.1155/2018/2571380.
Pełny tekst źródłaSavsani, Vimal, Vivek Patel, Bhargav Gadhvi i Mohamed Tawhid. "Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm". Modelling and Simulation in Engineering 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/2034907.
Pełny tekst źródłaQu, Dan, Xianfeng Ding i Hongmei Wang. "An Improved Multiobjective Algorithm: DNSGA2-PSA". Journal of Robotics 2018 (2.09.2018): 1–11. http://dx.doi.org/10.1155/2018/9697104.
Pełny tekst źródłaZhang, Maoqing, Lei Wang, Zhihua Cui, Jiangshan Liu, Dong Du i Weian Guo. "Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization". Mathematical Problems in Engineering 2020 (20.01.2020): 1–12. http://dx.doi.org/10.1155/2020/3094941.
Pełny tekst źródłaLiu, Yi, Jun Guo, Huaiwei Sun, Wei Zhang, Yueran Wang i Jianzhong Zhou. "Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model". Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8215308.
Pełny tekst źródłaXie, Yuan. "Fuzzy Parallel Machines Scheduling Problem Based on Genetic Algorithm". Advanced Materials Research 204-210 (luty 2011): 856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.856.
Pełny tekst źródłaDeng, Qianwang, Guiliang Gong, Xuran Gong, Like Zhang, Wei Liu i Qinghua Ren. "A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling". Computational Intelligence and Neuroscience 2017 (2017): 1–20. http://dx.doi.org/10.1155/2017/5232518.
Pełny tekst źródłaHou, Yaolong, Quan Yuan, Xueting Wang, Han Chang, Chenlin Wei, Di Zhang, Yanan Dong, Yijun Yang i Jipeng Zhang. "Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II". Buildings 14, nr 6 (17.06.2024): 1834. http://dx.doi.org/10.3390/buildings14061834.
Pełny tekst źródłaRozprawy doktorskie na temat "Nondominated sorting genetics algorithm (C-NSGA-II)"
Bouguila, Maissa. "Μοdélisatiοn numérique et οptimisatiοn des matériaux à changement de phase : applicatiοns aux systèmes cοmplexes". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR05.
Pełny tekst źródłaPhase-change materials exhibit considerable potential in the field of thermal management.These materials offer a significant thermal storage capacity. Excessive heat dissipated by miniature electronic components could lead to serious failures. A cooling system based on phase-change materials is among the most recommended solutions to guarantee the reliable performance of these microelectronic components. However, the low conductivity of these materials is considered a major limitation to their use in thermal management applications. The primary objective of this thesis is to address the challenge of improving the thermal conductivity of these materials. Numerical modeling is conducted, in the first chapters, to determine the optimal configuration of a heat sink, based on the study of several parameters such as fin insertion, nanoparticle dispersion, and the use of multiple phase-change materials. The innovation in this parametric study lies in the modeling of heat transfer from phase-change materials with relatively high nanoparticle concentration compared to the low concentration found in recent literature (experimental researchs). Significant conclusions are deducted from this parametric study, enabling us to propose a new model based on multiple phase-change materials improved with nanoparticles (NANOMCP). Reliable optimization studies are then conducted. Initially, a mono-objective reliability optimization study is carried out to propose a reliable and optimal model based on multiple NANOMCPs. The Robust Hybrid Method (RHM)proposes a reliable and optimal model, compared with the Deterministic Design Optimization method (DDO) and various Reliability Design Optimization methods (RBDO). Furthermore,the integration of a developed RBDO method (RHM) for the thermal management applicationis considered an innovation in recent literature. Additionally, a reliable multi-objective optimization study is proposed, considering two objectives: the total volume of the heat sink and the discharge time to reach ambient temperature. The RHM optimization method and the non-dominated sorting genetics algorithm (C-NSGA-II) were adopted to search for the optimal and reliable model that offers the best trade-off between the two objectives. Besides, An advanced metamodel is developed to reduce simulation time, considering the large number of iterations involved in finding the optimal model
Części książek na temat "Nondominated sorting genetics algorithm (C-NSGA-II)"
Lee, Ki-Baek. "D-NSGA-II: Dual-Stage Nondominated Sorting Genetic Algorithm-II". W Advances in Intelligent Systems and Computing, 291–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16841-8_27.
Pełny tekst źródłaNguyen, Thi-Thu-Thuy, Po-Chang Ko, Ping-Chen Li, Ming-Hung Shu, Yuh-Shiuan Wu, Min-Zhi Li i Wen-Hsien Chen. "Pairs Trading Selection Using Nondominated Sorting Genetic Algorithm (NSGA-II)". W Computational Intelligence Methods for Green Technology and Sustainable Development, 133–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19694-2_12.
Pełny tekst źródłaGoudos, Sotirios K. "Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems". W Multidisciplinary Computational Intelligence Techniques, 75–101. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch006.
Pełny tekst źródłaStreszczenia konferencji na temat "Nondominated sorting genetics algorithm (C-NSGA-II)"
Lim, Jae Hyung, i Rolf D. Reitz. "High Load (21bar IMEP) Dual Fuel RCCI Combustion Using Dual Direct Injection". W ASME 2013 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icef2013-19140.
Pełny tekst źródłaPatil, Pankaj, i Abhishek Abhishek. "Mission Based Design Optimization of Fixed Pitch Coaxial Propeller System for VTOL UAV". W Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14759.
Pełny tekst źródłaLiu, Y., C. Zhou i W. J. Ye. "A fast optimization method of using nondominated sorting genetic algorithm (NSGA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration". W 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547351.
Pełny tekst źródła