A Robust Global Optimization Algorithm of Electromagnetic Devices Utilizing Gradient Index and Multi-Objective Optimization Method

Ren, Ziyan; Pham, Minh-Trien; Song, Min‐Ho; Kim, Dong-Hun; Koh, Chang Seop

doi:10.1109/tmag.2010.2080664

Cited by 32 publications

(32 citation statements)

References 13 publications

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“…Here, the superscripts (1) , (2) , … , (M) indicate the sequential number of M sampling data for each variable x i (i = 1, 2, … , n). To accomplish variance estimation given in (6), extra sets of samples are needed to bring in.…”

Section: Proposed Optimization Using Variance Decomposition Methodsmentioning

confidence: 99%

“…In classical robust optimization, the sensitivity index of the objective function is consideredby estimating the maximum partial derivation, thus for a n-dimensional vector of design variables x 1 , x 2 , ... , x n , GI (x) defined as (2) Based on (2), the classical robust optimization algorithm is formulated as follows (3) where F is the target value of objective function f (x) assuming the design is a target-aimed design, whose objective function value is decided by the designers. Typically F is set to the optimal value obtained by solving the typical nonrobust optimization problem in (1).…”

Section: Classical Robust Optimization Using Gradient Indexmentioning

confidence: 99%

“…A number of robust optimization methods have been proposed to be applied to electromagnetic devices [2][3][4][5][6]. A large part of the optimization approaches use the gradient index (GI) of parameters to represent the robustness due to its simplicity and low computational cost [7].…”

Section: Introductionmentioning

confidence: 99%

“…A large part of the optimization approaches use the gradient index (GI) of parameters to represent the robustness due to its simplicity and low computational cost [7]. GI methods based on the sensitivity analysis (SA), especially the first-order SA, are extensively reported to evaluate the robustness of a design [2,3]. In order to provide more accurate results under uncertainty, higher order (i.e., secondorder) sensitivity analysis is employed in Ref.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Optimization Method Utilizing the Variance Decomposition Method for Electromagnetic Devices

Wang¹,

Li²,

Chen³

2014

Journal of Magnetics

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Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

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Section: Proposed Optimization Using Variance Decomposition Methodsmentioning

confidence: 99%

Section: Classical Robust Optimization Using Gradient Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Optimization Method Utilizing the Variance Decomposition Method for Electromagnetic Devices

Wang¹,

Li²,

Chen³

2014

Journal of Magnetics

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“…One way of incorporating robustness into the mainstream optimisation process is by adding the GI [4] as a second objective and formulating the problem as…”

Section: Multi-objective Robust Optimisation Using Gimentioning

confidence: 99%

Robust design optimisation of electromagnetic devices exploiting gradient indices and Kriging

Xiao

Rotaru

Sykulski

2015

IET Science, Measurement & Technology

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Since uncertainties in variables are unavoidable, an optimal solution must consider the robustness of the design. The gradient index approach provides a convenient way to evaluate the robustness but is inconclusive when several possible solutions exist. To overcome this limitation, a novel methodology based on the use of first-and second-order gradient indices is proposed introducing the notion of gradient sensitivity. The sensitivity affords a measure of the change in the objective function with respect to the uncertainty of the variables. A Kriging method assisted by algorithms exploiting the concept of rewards is employed to facilitate function predictions for the robust optimisation process. The performance of the proposed algorithm is assessed through a series of numerical experiments. A modification to the correlation model through the introduction of a Kriging predictor and mean square error criterion allows efficient solution of large scale and multi-parameter problems. The three-parameter version of TEAM Workshop Problem 22 has been used for illustration.

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OCRUN: An oppositional Runge Kutta optimizer with cuckoo search for global optimization and feature selection

Zhang

Chen

Heidari

et al. 2023

Applied Soft Computing

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A Robust Global Optimization Algorithm of Electromagnetic Devices Utilizing Gradient Index and Multi-Objective Optimization Method

Cited by 32 publications

References 13 publications

A Robust Optimization Method Utilizing the Variance Decomposition Method for Electromagnetic Devices

A Robust Optimization Method Utilizing the Variance Decomposition Method for Electromagnetic Devices

Robust design optimisation of electromagnetic devices exploiting gradient indices and Kriging

OCRUN: An oppositional Runge Kutta optimizer with cuckoo search for global optimization and feature selection

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