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Progress In Electromagnetics Research
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HYBRID-SURROGATE-MODEL-BASED EFFICIENT GLOBAL OPTIMIZATION FOR HIGH-DIMENSIONAL ANTENNA DESIGN

By L.-L. Chen, C. Liao, W. Lin, L. Chang, and X.-M. Zhong

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Abstract:
Efficient global optimization has been extensively used in problems with expensive cost functions. However, this method is not suitable for high-dimensional problems. In this paper, the radial basis function network is introduced into the efficient global optimization, to avoid local optima and achieve a fast convergence for high-dimensional optimization. Our algorithm is applied to a 12-dimensional optimization of a transmitting antenna. Compared to the genetic-algorithm-based efficient global optimization and the differential evolution strategy, our algorithm converges to the global optimal value more efficiently.

Citation:
L.-L. Chen, C. Liao, W. Lin, L. Chang, and X.-M. Zhong, "Hybrid-Surrogate-Model-Based Efficient Global Optimization for High-Dimensional Antenna Design," Progress In Electromagnetics Research, Vol. 124, 85-100, 2012.
doi:10.2528/PIER11121203
http://www.jpier.org/PIER/pier.php?paper=11121203

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