Efficient Global Optimization of MEMS Based on Surrogate Model Assisted Evolutionary Algorithm

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

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SADEA-II: A generalized method for efficient global optimization of antenna design

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2016.11.002 ◽

2016 ◽

Vol 4 (2) ◽

pp. 86-97 ◽

Cited By ~ 9

Author(s):

Bo Liu ◽

Slawomir Koziel ◽

Nazar Ali

Keyword(s):

Data Mining ◽

Global Optimization ◽

Design Optimization ◽

Surrogate Model ◽

Simulation Models ◽

Optimization Methods ◽

Optimization Method ◽

Antenna Design ◽

Efficient Global Optimization ◽

Optimization Framework

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

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Efficient Global Optimization Reliability Analysis (EGORA) for Time-Dependent Limit-State Functions

Volume 2B: 40th Design Automation Conference ◽

10.1115/detc2014-34281 ◽

2014 ◽

Author(s):

Zhen Hu ◽

Xiaoping Du

Keyword(s):

Monte Carlo Simulation ◽

Global Optimization ◽

Surrogate Model ◽

Limit State ◽

Time Dependent ◽

Efficient Global Optimization ◽

State Function ◽

Limit State Function ◽

Extreme Response ◽

Input Variables

If a limit-state function involves time, the associated reliability is defined within a period of time. The extreme value of the limit-state function is needed to calculate the time-dependent reliability, and the extreme value is usually highly nonlinear with respect to random input variables and may follow a multimodal distribution. For this reason, a surrogate model of the extreme response along with Monte Carlo simulation is usually employed. The objective of this work is to develop a new method, called the Efficient Global Optimization Reliability Analysis (EGORA), to efficiently build the surrogate model. EGORA is based on the Efficient Global Optimization (EGO) method. Different from the current method that generates training points for random variables and time independently, EGORA draws training points for the two types of input variables simultaneously and therefore accounts for their interaction effects. The other improvement is that EGORA only focuses on high accuracy at or near the limit state. With the two improvements, the new method can effectively reduce the number of training points. Once the surrogate model of the extreme response is available, Monte Carlo simulation is applied to calculate the time-dependent reliability. Good accuracy and efficiency of EGORA are demonstrated by three examples.

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HYBRID-SURROGATE-MODEL-BASED EFFICIENT GLOBAL OPTIMIZATION FOR HIGH-DIMENSIONAL ANTENNA DESIGN

Progress In Electromagnetics Research ◽

10.2528/pier11121203 ◽

2012 ◽

Vol 124 ◽

pp. 85-100 ◽

Cited By ~ 8

Author(s):

Ling-Lu Chen ◽

Cheng Liao ◽

Wenbin Lin ◽

Lei Chang ◽

Xuan-Ming Zhong

Keyword(s):

Global Optimization ◽

Surrogate Model ◽

Antenna Design ◽

High Dimensional ◽

Efficient Global Optimization ◽

Model Based

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The Effect of Multi-Additional Sampling for Multi-Fidelity Efficient Global Optimization

Symmetry ◽

10.3390/sym12091499 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1499

Author(s):

Atthaphon Ariyarit ◽

Tharathep Phiboon ◽

Masahiro Kanazaki ◽

Sujin Bureerat

Keyword(s):

Global Optimization ◽

Surrogate Model ◽

Computer Aided Design ◽

Computational Cost ◽

Sampling Technique ◽

Efficient Global Optimization ◽

Kriging Surrogate Model ◽

Engineering Product ◽

Sampling Points ◽

Aided Design

Powerful computer-aided design tools are presently vital for engineering product development. Efficient global optimization (EGO) is one of the most popular methods for design of a high computational cost problem. The original EGO is proposed for only one additional sample point. In this work, parallel computing is applied to the original EGO process via a multi-additional sampling technique. The weak point of the multi-additional sampling is it has slower convergence rate when compared with the original EGO. This paper applies the multi-fidelity technique to the multi-additional EGO process to see the effect of the number of multi-additional sampling points and the converge rate. A co-kriging method and a hybrid RBF/Kriging surrogate model are selected for the surrogate model in the EGO process to show the advantage of the multi-additional EGO process compared with the single-fidelity Kriging surrogate model. In the experiment, single-additional sampling points and two to four number of multi-additional sampling per iteration are tested with symmetry and asymmetry mathematical test functions. The results show the hybrid RBF/Kriging surrogate model can obtain the similar optimal points when using the multi-additional sampling EGO.

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