Akinsolu, M. O., Liu, B. , Lazaridis, P. I., Mistry, K. K., Mognaschi, M. E., Di Barba, P. and Zaharis, Z. D. (2020) Efficient design optimization of high-performance MEMS based on a surrogate-assisted self-adaptive differential evolution. IEEE Access, 8, pp. 80256-80268. (doi: 10.1109/ACCESS.2020.2990455)
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Abstract
High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermo-elastic micro-actuator, a high-performance corrugated membrane microactuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Liu, Dr Bo |
Authors: | Akinsolu, M. O., Liu, B., Lazaridis, P. I., Mistry, K. K., Mognaschi, M. E., Di Barba, P., and Zaharis, Z. D. |
College/School: | College of Science and Engineering > School of Engineering |
Journal Name: | IEEE Access |
Publisher: | IEEE |
ISSN: | 2169-3536 |
ISSN (Online): | 2169-3536 |
Published Online: | 27 April 2020 |
Copyright Holders: | Copyright © 2020 The Authors |
First Published: | First published in IEEE Access 8: 80256-80268 |
Publisher Policy: | Reproduced under a Creative Commons License |
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