A Generalized Method for Efficient Global Optimization of Antenna Design

Liu, Bo, Koziel, S and Ali, Nazar (2016) A Generalized Method for Efficient Global Optimization of Antenna Design. Journal of Computational Design and Engineering, 4 (2). pp. 86-97. ISSN 2288-4300

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Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality.

Item Type: Article
Depositing User: Hayley Dennis
Date Deposited: 13 May 2019 14:02
Last Modified: 13 May 2019 14:19
URI: https://glyndwr.repository.guildhe.ac.uk/id/eprint/17423

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