Fast Multi-Objective Optimization of Multi-Parameter Antenna Structures Based on Improved BPNN Surrogate Model

In this paper, a surrogate model based on a sparsely connected back propagation neural networks (SC-BPNN) is proposed to reduce the large computational cost of conventional multi-objective antenna optimization problems. In this model, the connection parameters and network structure can be adaptively...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.77692-77701
Main Authors: Dong, Jian, Qin, Wenwen, Wang, Meng
Format: Article
Language:English
Subjects:
Algorithms
Antenna design
Antennas
Artificial neural networks
Back propagation networks
BP neural network
Computational efficiency
Computational modeling
Computing costs
Global optimization
HPSO
Mathematical model
Mathematical models
multi-objective optimization
Multiple objective analysis
Neural networks
Optimization
Parameters
Pareto optimization
Particle swarm optimization
surrogate model
Training
transfer function
Transfer functions
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Summary:In this paper, a surrogate model based on a sparsely connected back propagation neural networks (SC-BPNN) is proposed to reduce the large computational cost of conventional multi-objective antenna optimization problems. In this model, the connection parameters and network structure can be adaptively tuned by a hybrid real-binary particle swarm optimization (HPSO) algorithm for better network global optimization capability. Also, a time-varying transfer function is introduced to improve the problem of easily trapping into local optimum and to accelerate network convergence. Further, a fast multi-objective optimization framework based on the proposed SC-BPNN is established for multi-parameter antenna structures. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, indicating that the proposed model predicts antenna performance more accurately and saves considerable computational cost compared to those previously published approaches.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2920945