
Proceedings Paper
Efficient global optimization of a limited parameter antenna designFormat | Member Price | Non-Member Price |
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Paper Abstract
Efficient Global Optimization (EGO) is a competent evolutionary algorithm suited for problems with limited design
parameters and expensive cost functions. Many electromagnetics problems, including some antenna designs, fall
into this class, as complex electromagnetics simulations can take substantial computational effort. This makes simple
evolutionary algorithms such as genetic algorithms or particle swarms very time-consuming for design optimization, as
many iterations of large populations are usually required. When physical experiments are necessary to perform
tradeoffs or determine effects which may not be simulated, use of these algorithms is simply not practical at all due to
the large numbers of measurements required. In this paper we first present a brief introduction to the EGO algorithm.
We then present the parasitic superdirective two-element array design problem and results obtained by applying EGO to
obtain the optimal element separation and operating frequency to maximize the array directivity. We compare these
results to both the optimal solution and results obtained by performing a similar optimization using the Nelder-Mead
downhill simplex method. Our results indicate that, unlike the
Nelder-Mead algorithm, the EGO algorithm did not
become stuck in local minima but rather found the area of the correct global minimum. However, our implementation
did not always drill down into the precise minimum and the addition of a local search technique seems to be indicated.
Paper Details
Date Published: 1 May 2008
PDF: 14 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640J (1 May 2008); doi: 10.1117/12.782997
Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)
PDF: 14 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640J (1 May 2008); doi: 10.1117/12.782997
Show Author Affiliations
Teresa H. O'Donnell, Air Force Research Lab. (United States)
ARCON Corp. (United States)
Hugh L. Southall, Air Force Research Lab. (United States)
Vistronix, Inc. (United States)
ARCON Corp. (United States)
Hugh L. Southall, Air Force Research Lab. (United States)
Vistronix, Inc. (United States)
Bryan Kaanta, Air Force Research Lab. (United States)
Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)
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