Fast Multi-Objective Antenna Design Based on BPNN Surrogate Model

2018 ◽  
Author(s):  
Wenwen Qin ◽  
Jian Dong ◽  
Yingjuan Li ◽  
Meng Wang
Keyword(s):  
Surrogate Model ◽  
Antenna Design ◽  
Multi Objective

scholarly journals Low-Cost Multi-Objective Optimization of Multiparameter Antenna Structures Based on the l1 Optimization BPNN Surrogate Model

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 839
Author(s):  
Dong ◽  
Qin ◽  
Mo
Keyword(s):  
Surrogate Model ◽  
Communication Systems ◽  
Low Cost ◽  
Computational Cost ◽  
Optimization Method ◽  
Antenna Design ◽  
Multi Objective Optimization ◽  
Compact Structure ◽  
Multi Objective ◽  
L1 Optimization

The development of modern wireless communication systems not only requires the antenna to be lightweight, low cost, easy to manufacture and easy to integrate but also imposes requirements on the miniaturization, wideband, and multiband design of the antenna. Therefore, designing an antenna that quickly and effectively meets multiple performance requirements is of great significance. To solve the problem of the large computational cost of traditional multi-objective antenna design methods, this paper proposes a backpropagation neural network surrogate model based on l1 optimization (l1-BPNN). The l1 optimization method tends to punish larger weight values and select smaller weight values so as to preserve a small amount of important weights and reset relatively unimportant weights to zero. By using l1 optimization method, the network mapping structure can be automatically adjusted to achieve the most suitable and compact structure of the surrogate model. Furthermore, for multi-parameter antenna design problems, a fast multi-objective optimization framework is constructed using the proposed l1-BPNN as a surrogate model. The framework is illustrated using a miniaturized multiband antenna design case, and a comparison with previously published methods, as well as numerical validation, is also provided.

scholarly journals Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm

2019 ◽  
Vol 9 (13) ◽  
pp. 2589 ◽  
Author(s):  
Jian Dong ◽  
Yingjuan Li ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Surrogate Model ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Search Space ◽  
Population Diversity ◽  
Antenna Design ◽  
Multi Objective ◽  
Antenna Performance ◽  
Improved Pso

In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches.

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 approach to optimize cure process for thick composite based on multi-field coupled model with RBF surrogate model

2021 ◽  
Vol 24 ◽  
pp. 100671
Author(s):  
Zhenyi Yuan ◽  
Lingfei Kong ◽  
Dajing Gao ◽  
Xinxing Tong ◽  
Yu Feng ◽  
...  
Keyword(s):  
Surrogate Model ◽  
Coupled Model ◽  
Cure Process ◽  
Multi Objective

Antenna design using dynamic multi‐objective evolutionary algorithm

2018 ◽  
Vol 12 (13) ◽  
pp. 2065-2072 ◽  
Author(s):  
Ruwang Jiao ◽  
Yongzhi Sun ◽  
Jianqing Sun ◽  
Yuhong Jiang ◽  
Sanyou Zeng
Keyword(s):  
Evolutionary Algorithm ◽  
Antenna Design ◽  
Multi Objective

scholarly journals Multi-Objective Optimization of a High Specific Speed Centrifugal Volute Pump Using 3D Inverse Design Coupled With CFD Simulations

2019 ◽  
Author(s):  
Luying Zhang ◽  
Gabriel Davila ◽  
Mehrdad Zangeneh
Keyword(s):  
Surrogate Model ◽  
Design Method ◽  
Inverse Design ◽  
Multi Objective Optimization ◽  
Blade Angle ◽  
Multi Objective ◽  
Specific Speed ◽  
Meridional Shape ◽  
Inverse Design Method ◽  
Conventional Optimization

Abstract This paper presents three different multi-objective optimization strategies for a high specific speed centrifugal volute pump design. The objectives of the optimization consist of maximizing the efficiency and minimizing the cavitation while maintaining the Euler head. The first two optimization strategies use a 3D inverse design method to parametrize the blade geometry. Both meridional shape and 3D blade geometry is changed during the optimization. In the first approach Design of Experiment method is used and the efficiency computed from CFD computations, while cavitation is evaluated by using minimum pressure on blade surface predicted by 3D inverse design method. The design matrix is then used to create a surrogate model where optimization is run to find the best tradeoff between cavitation and efficiency. This optimized geometry is manufactured and tested and is found to be 3.9% more efficient than the baseline with little cavitation at high flow. In the second approach the 3D inverse design method output is used to compute the efficiency and cavitation parameters and this leads to considerable reduction to the computational time. The resulting optimized geometry is found to be similar to the more computationally expensive solution based on 3D CFD results. In order to compare the inverse design based optimization to the conventional optimization an equivalent optimization is carried out by parametrizing the blade angle and meridional shape. Two different approaches are used for conventional optimization one in which the blade angle at TE is not constrained and one in which blade angles are constrained. In both cases larger variation in head is obtained when compared with the inverse design approach. This makes it impossible to create an accurate surrogate model. Furthermore, the efficiency levels in the conventional optimization is generally lower than the inverse design based optimization.

Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

2021 ◽  
pp. 283-299
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou ◽  
Chongyang Jiao ◽  
Ting Xu
Keyword(s):  
Test Data ◽  
Surrogate Model ◽  
Firefly Algorithm ◽  
Test Data Generation ◽  
Data Generation ◽  
Multi Objective
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