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This study introduces an adaptive optimization framework for micro-electromechanical systems (MEMS). Adaptive methods significantly improve solution quality and quantity while drastically reducing computational costs compared to traditional approaches.

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Area of Science:

  • Engineering
  • Computational Science

Background:

  • Traditional optimization methods for micro-electromechanical systems (MEMS) face limitations in handling objective constraints and providing diverse design options.
  • Existing techniques often require extensive computational resources, such as finite element method (FEM) simulations, limiting their practical application.

Purpose of the Study:

  • To present a comprehensive multi-objective optimization framework for MEMS design.
  • To integrate and evaluate traditional and adaptive optimization techniques, specifically Surrogate-Assisted Multi-Objective Optimization (SAMOO) and Adaptive-SAMOO (A-SAMOO).
  • To enhance the flexibility, applicability, and efficiency of MEMS optimization processes.

Main Methods:

  • Development of a multi-objective optimization framework incorporating both traditional and adaptive strategies.
  • Implementation of Surrogate-Assisted Multi-Objective Optimization (SAMOO) and its adaptive variant (A-SAMOO).
  • Testing and validation of the proposed methods on various MEMS devices, including Lorentz force and thermal actuators, with discrete design variables and objective constraints.

Main Results:

  • Adaptive optimization (A-SAMOO) significantly outperforms traditional methods in delivering a greater number and higher quality of optimal solutions.
  • The adaptive approach achieved high-quality solutions using only 2.8% of the FEM evaluations required by traditional methods without surrogate models.
  • Demonstrated robustness and versatility across different MEMS devices and complex design scenarios, including discrete variables and strict constraints.

Conclusions:

  • The proposed adaptive optimization framework enhances accuracy, speed, and solution diversity in MEMS design.
  • Online learning within the adaptive framework offers substantial advantages over traditional offline optimization techniques.
  • The framework provides a valuable, reliable, and effective resource for researchers and practitioners optimizing MEMS designs.