Optimization of Micro-Electromechanical Lorentz Actuator Using a Surrogate Model Accelerated Genetic Algorithm
A surrogate model (SM)-assisted multi-objective genetic algorithm (GA) is presented that is used to accelerate the performance optimization of a MEMS actuator. The GA employs both finite element method (FEM) simulations and the SM together to undertake the multi-objective optimization. The algorithm...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-04-01
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| Series: | Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3900/97/1/191 |
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| Summary: | A surrogate model (SM)-assisted multi-objective genetic algorithm (GA) is presented that is used to accelerate the performance optimization of a MEMS actuator. The GA employs both finite element method (FEM) simulations and the SM together to undertake the multi-objective optimization. The algorithm evolves the actuator geometry to meet a required 1 µm displacement while seeking to achieve the objectives of minimal temperature rise and resonant frequency over 5 kHz. The result is a continuous surface of Pareto optimal designs for the decision maker to choose from. The SM was found to compute similar solutions as the FEM with a 100,000× faster computation speed. |
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| ISSN: | 2504-3900 |