Fast parameter optimization of large-scale electromagnetic objects using DIRECT with Kriging metamodeling

With the advent of fast methods to significantly speed up numerical computation of large-scale realistic electromagnetic (EM) structures, EM design and optimization is becoming increasingly attractive. In recent years, genetic algorithms, neural network and evolutionary optimization methods have bec...

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Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques 2004-01, Vol.52 (1), p.276-285
Main Authors: Eng Swee Siah, Sasena, M., Volakis, J.L., Papalambros, P.Y., Wiese, R.W.
Format: Article
Language:English
Subjects:
Constraint optimization
Design optimization
Genetic algorithms
Large-scale systems
Metamodeling
Neural networks
Operations research
Optimization methods
Radio frequency
Shape
Slot antennas
Studies
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Summary:With the advent of fast methods to significantly speed up numerical computation of large-scale realistic electromagnetic (EM) structures, EM design and optimization is becoming increasingly attractive. In recent years, genetic algorithms, neural network and evolutionary optimization methods have become increasingly popular for EM optimization. However, these methods are usually associated with a slow convergence bound and, furthermore, may not yield a deterministic optimal solution. In this paper, a new hybrid method using Kriging metamodeling in conjunction with the divided rectangles (DIRECT) global-optimization algorithm is used to yield a globally optimal solution efficiently. The latter yields a deterministic answer with fast convergence bounds and inherits both local and global-optimization properties. Three examples are given to illustrate the applicability of the method, Le., shape optimization for a slot-array frequency-selective surface, antenna location optimization to minimize EM coupling from the antenna to RF devices in automobile structures, and multisensor optimization to satisfy RF coupling constraints on a vehicular chassis in the presence of a wire harness. In the first example, DIRECT with Kriging surrogate modeling was employed. In the latter two examples, the adaptive hybrid optimizer, superEGO, was used. In all three examples, emphasis is placed on the speed of convergence, as well as on the flexibility of the optimization algorithms.
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2003.820891