Optimization of U-Shaped Flow Channel by Rbfnn and Nsga-Ii

Li, Mandi; Liu, Xiangyu; Kong, Decai; Guo, Qi; Wang, Yupeng; Yao, Jing

doi:10.2139/ssrn.4244078

Cited by 1 publication

(1 citation statement)

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“…The NSGA-Ⅱ algorithm was proposed by Srinivas and Deb on the basis of NSGA in 2000, and it uses a fast no n-dominated sorting algorithm, and its computational complexity is greatly reduced compared with NSGA; it introduces elite strategy, expands the sampling space, prevents the loss of the best individual, and improves the operation speed of the algorithm [11]. The control parameters of the NSGA-II algorithm are listed in Table 5.…”

Section: Optimization Algorithm Selectionmentioning

confidence: 99%

Multi-objective optimization of pure electric SUV transmission system parameter based on non-dominated sorted genetic algorithm II

Naifa,

Hao,

fan

et al. 2023

Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023)

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Research was carried out on the pure electric sport utility vehicle (SUV) transmission system coupled with 6-speed dual clutch transmission (DCT). Based on AVL Cruise software, the car is modeled, and the software simulation results meet the constraints. To further improve the performance, it is equipped with the joint simulation model of Isight and Cruise, adopts non-dominated sorted genetic algorithm II (NSGA-II), takes the transmission ratio of the pure electric SUV transmission system as the optimization parameter, the NEDC cycle The power consumption per 100 kilometers and the acceleration time to 100 kilometers under working conditions are optimized by multi-objective optimization calculations. The optimization results are entered into cruise software for review, and the outcome shows that, compared with before optimization, the power consumption per 100 kilometers of the NEDC cycle is reduced by 1.39kWh, and the economy is improved by 11.02%; the acceleration time to 100km/h is reduced by 0.81s, and the dynamic performance is increased by 7.03%

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Section: Optimization Algorithm Selectionmentioning

confidence: 99%