Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution

Mobayode O. Akinsolu, Bo Liu, Pavlos I. Lazaridis, Keyur K. Mistry, Maria Evelina Mognaschi, Paolo Di Barba, Zaharias D. Zaharis

Research output: Contribution to journalArticle

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 micro-actuator, 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.

Original languageEnglish
Article number9078676
Pages (from-to)80256-80268
Number of pages13
JournalIEEE Access
Volume8
Early online date27 Apr 2020
DOIs
Publication statusPublished - 13 May 2020

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