Abstract
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.
Highlights
The ever-increasing demands of modern wireless communications, including 4G/5G, wireless sensor networks, and Internet of Things (IoT), require antenna designs to handle multiple objectives, e.g., wideband or multi-band, high gain or efficiency, compact size, etc. In this circumstance, automated antenna optimization based on multi-objective evolutionary algorithms (MOEAs), such as genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], and multi-objective optimization algorithm based on decomposition (MOEA/D) [3], provide a new path for antenna designers because of their strong capabilities of simultaneously handling multiple design objectives and optimizing multiple design parameters
To overcome the above drawbacks, we propose an improved PSO to optimize radial basis function neural network (RBFNN) parameters for improving network convergence and generalization capability
The results indicate that the use of various surrogate models greatly reduces the computational time compared to HFSS simulation
Summary
The ever-increasing demands of modern wireless communications, including 4G/5G, wireless sensor networks, and Internet of Things (IoT), require antenna designs to handle multiple objectives, e.g., wideband or multi-band, high gain or efficiency, compact size, etc. Gradient-based strategies, such as back-propagation (BP) algorithm [21], were applied to adjust the RBFNN parameters for better network performance. Such approaches may have limited searching capability to find the global minimum [22]. The advantages of the proposed PSO-RBFNN over other RBFNNs exist in better network convergence and prediction accuracy, which are verified by a design case of a planar miniaturized triple-band antenna. This rest of the paper is organized as follows.
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