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An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques

Liu, Bo, Aliakbarian, H, Ma, Z, Vandenbosch, G, Gielen, G and Excell, Peter S (2013) An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques. IEEE Transactions on Antennas & Propagation, 62 (1). pp. 7-18. ISSN 0018-926X

Liu_An_efficient_method_for_antenna_design_optimization.pdf - Accepted Version

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In recent years, various methods from the evolutionary computation (EC) field have been applied to electromagnetic (EM) design problems and have shown promising results. However, due to the high computational cost of the EM simulations, the efficiency of directly using evolutionary algorithms is often very low (e.g., several weeks' optimization time), which limits the application of these methods for many industrial applications. To address this problem, a new method, called surrogate model assisted differential evolution for antenna synthesis (SADEA), is presented in this paper. The key ideas are: (1) A Gaussian Process (GP) surrogate model is constructed on-line to predict the performances of the candidate designs, saving a lot of computationally expensive EM simulations. (2) A novel surrogate model-aware evolutionary search mechanism is proposed, directing effective global search even when a traditional high-quality surrogate model is not available. Three complex antennas and two mathematical benchmark problems are selected as examples. Compared with the widely used differential evolution and particle swarm optimization, SADEA can obtain comparable results, but achieves a 3 to 7 times speed enhancement for antenna design optimization.

Item Type: Article
Keywords: Antenna design optimization, Gaussian process, antenna synthesis, differential evolution, efficient global optimization, expensive black-box optimization, surrogate model assisted evolutionary algorithm
Divisions: Social and Life Sciences
Depositing User: Mr Stewart Milne
Date Deposited: 11 Aug 2015 15:08
Last Modified: 19 Dec 2017 15:29

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