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

Liu, B. and Nikolaeva, A. (2016) Efficient Global Optimization of MEMS Based on Surrogate Model Assisted Evolutionary Algorithm. In: 2016 Design, Automation and Test in Europe Conference and Exhibition (DATE), Dresden, Germany, 14-18 Mar 2016, pp. 555-558. ISBN 9783981537079

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Publisher's URL: https://ieeexplore.ieee.org/document/7459373


Optimization plays a key role in MEMS design. However, most MEMS design optimization (exploration) methods either depend on ad-hoc analytical / behavioural models or time consuming numerical simulations. Surrogate modeling techniques have been introduced to integrate generality and efficiency, but the number of design variables which can be handled by most existing efficient MEMS design optimization methods is often less than 5. To address the above challenges, a new method, called Adaptive Gaussian Process-Assisted Differential Evolution for MEMS Design Optimization (AGDEMO) is proposed. The key idea is the proposed ON-LINE adaptive surrogate model assisted optimization framework. In particular, AGDEMO performs global optimization of MEMS using numerical simulation and the differential evolution (DE) algorithm, and a Gaussian process surrogate model is constructed ONLINE to predict the results of expensive numerical simulations. AGDEMO is tested by two actuators (both with 9 design variables). Comparisons with state-of-the-art methods verify advantages of AGDEMO in terms of efficiency, optimization capacity and scalability.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Liu, Dr Bo
Authors: Liu, B., and Nikolaeva, A.
College/School:College of Science and Engineering > School of Engineering
Published Online:28 April 2016

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