Efficient AI-Driven Design of Microwave Antennas Using PSADEA

Nowadays, artificial intelligence plays a very significant role in the optimal design of microwave devices such as antennas. In particular, antenna design automation via surrogate model-based optimization (SMBO) methods is attracting a lot of interest due to the efficiency improvement in terms of computational cost. The parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA) method is a state-of-the-art SMBO method. In this paper, PSADEA is used to synthesize a compact slotted monopole antenna for ultra wide band body-centric applications. The performance of PSADEA is compared with 2019 Computer Simulation Technology - Microwave Studio (CST-MWS) optimizers: trust region framework (TRF) and particle swarm optimisation (PSO). Results from the comparisons show that PSADEA obtains very satisfactory design solutions for the monopole antenna in all runs using an affordable optimization time in each, whereas the alternative optimizers fail to obtain a satisfactory design solution in all runs. A close agreement between the simulated and measured results for the fabricated prototype of a typical PSADEA synthesized design for the monopole antenna validates the design solution quality of PSADEA.

[1]  Vic Grout,et al.  Efficient Global Optimization of Actuator Based on a Surrogate Model Assisted Hybrid Algorithm , 2018, IEEE Transactions on Industrial Electronics.

[2]  Lei Wang,et al.  Antenna Array Design by Artificial Bee Colony Algorithm With Similarity Induced Search Method , 2019, IEEE Transactions on Magnetics.

[3]  Yifan Chen,et al.  A Q-Slot Monopole for UWB Body-Centric Wireless Communications , 2017, IEEE Transactions on Antennas and Propagation.

[4]  M. Takahashi,et al.  A Compact Planar Inverted-F Antenna for 2.45 GHz On-Body Communications , 2012, IEEE Transactions on Antennas and Propagation.

[5]  Guy A. E. Vandenbosch,et al.  An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques , 2014, IEEE Transactions on Antennas and Propagation.

[6]  Slawomir Koziel,et al.  Sequential approximate optimisation for statistical analysis and yield optimisation of circularly polarised antennas , 2018, IET Microwaves, Antennas & Propagation.

[7]  Jose Ruiz-Pinales,et al.  A Hybrid Method to Design Wire Antennas: Design and optimization of antennas using artificial intelligence. , 2015, IEEE Antennas and Propagation Magazine.

[8]  Raed A. Abd-Alhameed,et al.  Antenna Design Explorer: A GUI Software Tool for Efficient Antenna Design Optimization , 2017 .

[9]  Vic Grout,et al.  GUI design exploration software for microwave antennas , 2017, J. Comput. Des. Eng..

[10]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.

[11]  Bo Liu,et al.  Global Optimization of Microwave Filters Based on a Surrogate Model-Assisted Evolutionary Algorithm , 2017, IEEE Transactions on Microwave Theory and Techniques.

[12]  Raed A. Abd-Alhameed,et al.  Design and optimization of a bowtie dipole adjacent to dielectric material for through the wall imaging using hybrid electromagnetic computational analysis techniques , 2017, 2017 Internet Technologies and Applications (ITA).

[13]  A. Faraone,et al.  Comparisons of computed mobile phone induced SAR in the SAM phantom to that in anatomically correct models of the human head , 2006, IEEE Transactions on Electromagnetic Compatibility.

[14]  Slawomir Koziel,et al.  SADEA-II: A generalized method for efficient global optimization of antenna design , 2016, J. Comput. Des. Eng..

[15]  Slawomir Koziel,et al.  Performance-Based Nested Surrogate Modeling of Antenna Input Characteristics , 2019, IEEE Transactions on Antennas and Propagation.

[16]  Nor Ashidi Mat Isa,et al.  Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review , 2019, IEEE Access.

[17]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[18]  Christophe Roblin,et al.  Stochastic Surrogate Models of Deformable Antennas Based on Vector Spherical Harmonics and Polynomial Chaos Expansions: Application to Textile Antennas , 2018, IEEE Transactions on Antennas and Propagation.

[19]  T. Weiland,et al.  A practical guide to 3-D simulation , 2008, IEEE Microwave Magazine.

[20]  Raed A. Abd-Alhameed,et al.  Efficient global optimisation of microwave antennas based on a parallel surrogate model‐assisted evolutionary algorithm , 2018, IET Microwaves, Antennas & Propagation.

[21]  Slawomir Koziel,et al.  Reliable Multistage Optimization of Antennas for Multiple Performance Figures in Highly Dimensional Parameter Spaces , 2019, IEEE Antennas and Wireless Propagation Letters.

[22]  Pavlos I. Lazaridis,et al.  A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization , 2019, IEEE Transactions on Emerging Topics in Computational Intelligence.

[23]  M. Stein.  Large sample properties of simulations using latin hypercube sampling , 1987 .

[24]  Bo Liu,et al.  Time and Frequency Domain Simulation, Measurement and Optimization of Log-Periodic Antennas , 2019, Wirel. Pers. Commun..

[25]  Douglas H. Werner,et al.  Efficient Multiobjective Antenna Optimization With Tolerance Analysis Through the Use of Surrogate Models , 2018, IEEE Transactions on Antennas and Propagation.

[26]  Pavlos I. Lazaridis,et al.  Software Solutions for Antenna Design Exploration: A Comparison of Packages, Tools, Techniques, and Algorithms for Various Design Challenges , 2019, IEEE Antennas and Propagation Magazine.