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

O'Donnell, T.H.; Southall, H.L.; Santarelli, Scott; Steyskal, H.

doi:10.1117/12.851793

Cited by 2 publications

(3 citation statements)

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“…The surrogate model employed in the EGO algorithm [1,[22][23][24][25][26][27]] is the Kriging model, which can be written as…”

Section: The Conventional Ego Algorithmmentioning

confidence: 99%

“…However, it is difficult for the conventional EGO to avoid falling into local optima when the dimensions of optimization increase [24,25]. In order to overcome this difficulty, some improved EGO algorithms have been proposed for high-dimensional problems, e.g., the Taguchi'smethod-based EGO [26] and the GA-based EGO [27]. However, the convergence rates of these methods remain to be improved.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with the population-based optimization techniques, such as the genetic algorithm (GA) [2][3][4][5], particle swarm optimization (PSO) [6][7][8][9][10][11][12][13][14] and differential evolution strategy (DES) [15][16][17][18][19][20][21], the EGO requires fewer function evaluations to obtain the global optimum [22][23][24][25][26][27]. However, it is difficult for the conventional EGO to avoid falling into local optima when the dimensions of optimization increase [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid-Surrogate-Model-Based Efficient Global Optimization for High-Dimensional Antenna Design

Chen¹,

Liao²,

Lin³

et al. 2012

PIER

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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.

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“…The surrogate model employed in the EGO algorithm [1,[22][23][24][25][26][27]] is the Kriging model, which can be written as…”

Section: The Conventional Ego Algorithmmentioning

confidence: 99%