Applying EGO to large dimensional optimizations: a wideband fragmented patch example

Teresa H. O'Donnell; Hugh Southall; Scott Santarelli; Hans Steyskal

doi:10.1117/12.851793

15 April 2010 Applying EGO to large dimensional optimizations: a wideband fragmented patch example

Teresa H. O'Donnell, Hugh Southall, Scott Santarelli, Hans Steyskal

Proceedings Volume 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV; 770407 (2010) https://doi.org/10.1117/12.851793
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Efficient Global Optimization (EGO) minimizes expensive cost function evaluations by correlating evaluated parameter sets and respective solutions to model the optimization space. For optimizations requiring destructive testing or lengthy simulations, this computational overhead represents a desirable tradeoff. However, the inspection of the predictor space to determine the next evaluation point can be a time-intensive operation. Although DACE predictor evaluation may be conducted for limited parameters by exhaustive sampling, this method is not extendable to large dimensions. We apply EGO here to the 11-dimensional optimization of a wide-band fragmented patch antenna and present an alternative genetic algorithm approach for selecting the next evaluation point. We compare results achieved with EGO on this optimization problem to previous results achieved with a genetic algorithm.

Citation Download Citation

Teresa H. O'Donnell, Hugh Southall, Scott Santarelli, and Hans Steyskal "Applying EGO to large dimensional optimizations: a wideband fragmented patch example", Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 770407 (15 April 2010); https://doi.org/10.1117/12.851793

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