|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4952983||1364507||2017||12 صفحه PDF||سفارش دهید||دانلود کنید|
- An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations.
- A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization
- A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).
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.
Journal: Journal of Computational Design and Engineering - Volume 4, Issue 2, April 2017, Pages 86-97