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Abstract
In this paper, an optimization framework for multi-objective design of antenna structures is discussed which exploits data-driven surrogates, a multi-objective evolutionary algorithm, response correction techniques for design refinement, as well as generalized domain segmentation. The last mechanism is introduced to constrain the design space region subjected to sampling, which permits reduction of the number of training data samples required for surrogate model identification. The generalized segmentation technique works for any number of design objectives. Here, it is demonstrated using a three-objective case study of a UWB monopole optimized for best in-band reflection, minimum gain variability, and minimum size. The numerical results indicate that segmentation leads to reducing the cost of initial Pareto identification by around 21 percent as compared to the conventional surrogate-assisted approach.
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- Accepted or Published Version
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- Copyright (2018 Warsaw Univ. of Technology, IEEE)
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 22nd International Microwave and Radar Conference (MIKON) strony 348 - 351
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Kozieł S., Bekasiewicz A.: Three-objective antenna optimization by means of kriging surrogates and domain segmentation// 2018 22nd International Microwave and Radar Conference (MIKON)/ : , 2018, s.348-351
- DOI:
- Digital Object Identifier (open in new tab) 10.23919/mikon.2018.8405222
- Verified by:
- Gdańsk University of Technology
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