Littérature scientifique sur le sujet « Nondominated sorting genetics algorithm (C-NSGA-II) »
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Articles de revues sur le sujet "Nondominated sorting genetics algorithm (C-NSGA-II)"
Maximov, Jordan, Galya Duncheva, Angel Anchev, Vladimir Dunchev, Vladimir Todorov et 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, no 12 (24 novembre 2023) : 1930. http://dx.doi.org/10.3390/met13121930.
Texte intégralZhang, Weipeng, Ke Wang et Chang Chen. « Artificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries ». Foods 11, no 21 (25 octobre 2022) : 3347. http://dx.doi.org/10.3390/foods11213347.
Texte intégralGong, Guiliang, Qianwang Deng, Xuran Gong, Like Zhang, Haibin Wang et He Xie. « A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery ». Mathematical Problems in Engineering 2018 (19 juin 2018) : 1–21. http://dx.doi.org/10.1155/2018/2571380.
Texte intégralSavsani, Vimal, Vivek Patel, Bhargav Gadhvi et 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.
Texte intégralQu, Dan, Xianfeng Ding et Hongmei Wang. « An Improved Multiobjective Algorithm : DNSGA2-PSA ». Journal of Robotics 2018 (2 septembre 2018) : 1–11. http://dx.doi.org/10.1155/2018/9697104.
Texte intégralZhang, Maoqing, Lei Wang, Zhihua Cui, Jiangshan Liu, Dong Du et Weian Guo. « Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization ». Mathematical Problems in Engineering 2020 (20 janvier 2020) : 1–12. http://dx.doi.org/10.1155/2020/3094941.
Texte intégralLiu, Yi, Jun Guo, Huaiwei Sun, Wei Zhang, Yueran Wang et 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.
Texte intégralXie, Yuan. « Fuzzy Parallel Machines Scheduling Problem Based on Genetic Algorithm ». Advanced Materials Research 204-210 (février 2011) : 856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.856.
Texte intégralDeng, Qianwang, Guiliang Gong, Xuran Gong, Like Zhang, Wei Liu et 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.
Texte intégralHou, Yaolong, Quan Yuan, Xueting Wang, Han Chang, Chenlin Wei, Di Zhang, Yanan Dong, Yijun Yang et Jipeng Zhang. « Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II ». Buildings 14, no 6 (17 juin 2024) : 1834. http://dx.doi.org/10.3390/buildings14061834.
Texte intégralThèses sur le sujet "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.
Texte intégralPhase-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
Chapitres de livres sur le sujet "Nondominated sorting genetics algorithm (C-NSGA-II)"
Lee, Ki-Baek. « D-NSGA-II : Dual-Stage Nondominated Sorting Genetic Algorithm-II ». Dans 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.
Texte intégralNguyen, Thi-Thu-Thuy, Po-Chang Ko, Ping-Chen Li, Ming-Hung Shu, Yuh-Shiuan Wu, Min-Zhi Li et Wen-Hsien Chen. « Pairs Trading Selection Using Nondominated Sorting Genetic Algorithm (NSGA-II) ». Dans 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.
Texte intégralGoudos, Sotirios K. « Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems ». Dans Multidisciplinary Computational Intelligence Techniques, 75–101. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch006.
Texte intégralActes de conférences sur le sujet "Nondominated sorting genetics algorithm (C-NSGA-II)"
Lim, Jae Hyung, et Rolf D. Reitz. « High Load (21bar IMEP) Dual Fuel RCCI Combustion Using Dual Direct Injection ». Dans ASME 2013 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icef2013-19140.
Texte intégralPatil, Pankaj, et Abhishek Abhishek. « Mission Based Design Optimization of Fixed Pitch Coaxial Propeller System for VTOL UAV ». Dans Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14759.
Texte intégralLiu, Y., C. Zhou et 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 ». Dans 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547351.
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