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

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 a...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.80256-80268
Main Authors: Akinsolu, Mobayode O., Liu, Bo, Lazaridis, Pavlos I., Mistry, Keyur K., Mognaschi, Maria Evelina, Barba, Paolo Di, Zaharis, Zaharias D.
Format: Article
Language:English
Subjects:
Adaptive systems
Computational modeling
Design optimization
Design specifications
differential evolution
Evolutionary computation
Gaussian process
high-performance MEMS design
MEMS design optimization
Microactuators
Microelectromechanical systems
Micromechanical devices
Mutation
Numerical analysis
Numerical models
Performance evaluation
Shape
Shape optimization
surrogate model assisted evolutionary algorithm
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2990455