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.
Highlights
Microelectromechanical systems (MEMS) with high performance requirements are widely used in modern engineering systems [1]
Present MEMS shape optimization methods can be divided into two categories, which are: (1) local optimization informed by design expertise and (2) evolutionary algorithms (EAs), which aim to perform global optimization
assisted differential evolution for MEMS optimization (ASDEMO) is proposed for high-performance MEMS shape optimization, it is interesting to investigate its optimization ability compared with L-SHADE
Summary
Microelectromechanical systems (MEMS) with high performance requirements are widely used in modern engineering systems [1]. Design expertise is firstly used to simplify the optimization problem by providing an initial design, narrowing down the search ranges and reducing the number of design variables based on sensitivity, [4]–[6] This kind of method is playing an important role in modern high-performance MEMS optimization. Substantial optimization efficiency improvement without compromising performance is highly desirable To address this problem, off-line surrogate model-based EAs (SAEAs) are introduced into MEMS shape optimization [21]. Efficient for various MEMS, high-performance MEMS requires a very accurate surrogate model to meet the stringent constraints and obtain a highly optimized objective function value. Even though efficient state-of-the-art online SAEAs have been proposed [25]–[27], to the best of our knowledge, these methods are arguably not purpose-built shape optimization methods for high-performance MEMS which need to meet highly stringent specifications.
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