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Open AccessArticle

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

School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Appl. Sci. 2019, 9(13), 2589; https://doi.org/10.3390/app9132589
Received: 10 May 2019 / Revised: 19 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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. View Full-Text
Keywords: antenna design; radial basis function neural networks (RBFNNs); particle swarm optimization (PSO); multi-objective optimization antenna design; radial basis function neural networks (RBFNNs); particle swarm optimization (PSO); multi-objective optimization
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Dong, J.; Li, Y.; Wang, M. Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm. Appl. Sci. 2019, 9, 2589.

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