Endgame implementations for the Efficient Global Optimization (EGO) algorithm

Southall, H.L.; O'Donnell, T.H.; Kaanta, Bryan

doi:10.1117/12.820460

Cited by 3 publications

(3 citation statements)

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“…At present, the optimal linear constraints [3,4], adaptive array [5,6] and genetic algorithm [7,8,9] or other intelligent optimization algorithm [10,11] are three representative numerical pattern synthesis methods for arbitrary array. The appearance of these methods brings great help and enriches the synthesis methods of the other array form other than uniform linear array.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Compact Receive Array in Industrial Wireless Sensor Networks

Wan

Han

Jiang

et al. 2016

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)

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Abstract-With the development of wireless communication, industrial wireless sensor networks (IWSNs) plays an important role in monitoring and control systems. In this paper, we extend the application of IWSNs into High Frequency Surface-Wave Radar (HFSWR) system. The traditional antenna is replaced by mobile IWSNs. In combination of the application precondition of super-directivity in HF band and circular topology of IWSNs, a super-directivity synthesis method is presented for designing super-directivity array. In this method, the dominance of external noise is ensured by constraining the Ratio of External to Internal Noise (REIN) of the array, and the desired side lobe level is achieved by implementing linear constraint. By using this method, the highest directivity will be achieved in certain conditions. Using the designed super directive circular array as sub-arrays, the compact receive antenna array is constructed, the purpose of miniaturization is achieved. Simulation verifies that the proposed method is correct and effective, the validity of the proposed method has been proved.

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Section: Introductionmentioning

confidence: 99%

Optimal Design of Compact Receive Array in Industrial Wireless Sensor Networks

Wan

Han

Jiang

et al. 2016

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)

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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%

“…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%

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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Accelerating optimization by tracing valley

2016

Computer Physics Communications

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Endgame implementations for the Efficient Global Optimization (EGO) algorithm

Cited by 3 publications

References 0 publications

Optimal Design of Compact Receive Array in Industrial Wireless Sensor Networks

Optimal Design of Compact Receive Array in Industrial Wireless Sensor Networks

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

Accelerating optimization by tracing valley

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