Multi-component optimization of a vertical inline pump based on multi-objective pso and artificial neural network

2020 ◽  
Vol 34 (12) ◽  
pp. 4883-4896
Author(s):  
Xingcheng Gan ◽  
Ji Pei ◽  
Wenjie Wang ◽  
Shouqi Yuan ◽  
Yajing Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multi Objective ◽  
Artificial Neural

Inverse Artificial Neural Network for Multi-Objective Antenna Design

2021 ◽  
pp. 1-1
Author(s):  
Li-Ye Xiao ◽  
Wei Shao ◽  
Fu-Long Jin ◽  
Bing-Zhong Wang ◽  
Qing Huo Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Antenna Design ◽  
Multi Objective ◽  
Artificial Neural

Multi-objective feed rate optimization of three-axis rough milling based on artificial neural network

2021 ◽  
Vol 114 (5-6) ◽  
pp. 1323-1339
Author(s):  
Jiejun Xie ◽  
Pengyu Zhao ◽  
Pengcheng Hu ◽  
Yang Yin ◽  
Huicheng Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feed Rate ◽  
Multi Objective ◽  
Rough Milling ◽  
Artificial Neural ◽  
Rate Optimization ◽  
Feed Rate Optimization

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

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