Multiple-scale uncertainty optimization design of hybrid composite structures based on neural network and genetic algorithm

2020 ◽  
pp. 113371
Author(s):  
Xiang Peng ◽  
Chan Qiu ◽  
Jiquan Li ◽  
Huaping Wu ◽  
Zhenyu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hybrid Composite ◽  
Composite Structures ◽  
Optimization Design ◽  
Multiple Scale ◽  
Scale Uncertainty ◽  
Uncertainty 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.

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

2014 ◽  
Vol 951 ◽  
pp. 274-277 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Proposed Model ◽  
Crossover Probability ◽  
Ga Algorithm ◽  
The Hierarchical Structure ◽  
Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

Optimal Design of Gears by Means of Genetic Algorithm and Neural Network

2013 ◽  
Vol 397-400 ◽  
pp. 816-820
Author(s):  
Yong Gang Li, ◽  
Yong Mei Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimal Solution ◽  
Constraint Equation ◽  
Relative Parameter

Optimal design of gears was complicated with much difficulty to determine the parameter of strength constraint equation, and find the optimal solution. Used BP Neural Network to approximate the relative parameter of gears optimization design which was shown by chart. Used Genetic Algorithm to search the optimal solution. The result shows that the application of Genetic Algorithm and Neural Network in gear optimization is effective.

Optimization Design of Penetrator Geometry Using Artificial Neural Network and Genetic Algorithm

2020 ◽  
Vol 37 (6) ◽  
pp. 429-436
Author(s):  
Kyu-Seok Jung ◽  
Sung-Min Cho ◽  
Jae-Hyeong Yu ◽  
Yo-Han Yoo ◽  
Jong-Bong Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Artificial Neural

Composite Structure Optimization Design System Based on Grid Technology

2007 ◽  
Vol 334-335 ◽  
pp. 453-456
Author(s):  
Wen Yuan Cheng ◽  
De Gang Cui ◽  
Yan Chang ◽  
Xiang Hui Xie
Keyword(s):  
Genetic Algorithm ◽  
Composite Structure ◽  
Composite Structures ◽  
Optimization Design ◽  
Structure Design ◽  
Design System ◽  
Grid Technology ◽  
Great Effort ◽  
Optimization Approach ◽  
Element Analysis

In the traditional iterative design process for composite structures, it is difficult to achieve an optimal solution even though a great effort is made. A genetic optimization system based on grid technology offers an automatic and efficient approach for composite structure redesign and optimization. A genetic algorithm system, which integrates Genetic Algorithm Optimization (GAO) software and a Finite Element Analysis (FEA) based commercial package, has been developed as a tool for composite structure design and analysis. The GAO is capable of tailoring large number of composite design variables and taking the time-consuming FEA results to calculate objective function value and conduct optimization in high accuracy. By operating the system employing the Grid technology and Artificial Neural Network (ANN) method, significant time saving in numerical analysis can be achieved. A user friendly interface has also been built in the system. In the paper, aeroelastic tailoring of a composite wing has been taken as a numerical example to demonstrate the optimization approach. The numerical results show that an optimal design has been achieved to meet the design requirement.

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