使用自适应替代模型来加速MEMS的多目标设计优化.
Ali Nazari1, Armin Aghajani1, Phiona Buhr1
1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Micromachines
|July 30, 2025
概括
本研究介绍了微电子机械系统 (MEMS) 的自适应优化框架. 与传统方法相比,自适应方法显著提高了解决方案的质量和数量,同时大大降低了计算成本.
科学领域:
- 工程 工程师 工程师 工程师
- 计算科学 计算科学
背景情况:
- 微机电系统 (MEMS) 的传统优化方法在处理客观约束和提供多样化的设计选项方面存在局限性.
- 现有的技术往往需要大量的计算资源,例如有限元法 (FEM) 模拟,这限制了它们的实际应用.
研究的目的:
- 为MEMS设计提供一个全面的多目标优化框架.
- 整合和评估传统和自适应优化技术,特别是代理辅助多目标优化 (SAMOO) 和自适应-SAMOO (A-SAMOO).
- 提高MEMS优化流程的灵活性,适用性和效率.
主要方法:
- 开发一个多目标优化框架,包括传统和适应性策略.
- 代理辅助多目标优化 (SAMOO) 和其自适应变体 (A-SAMOO) 的实施.
- 在各种MEMS设备上测试和验证拟议的方法,包括洛伦茨力和热驱动器,具有离散的设计变量和客观约束.
主要成果:
- 适应性优化 (A-SAMOO) 在提供更多和更高质量的最佳解决方案方面明显优于传统方法.
- 适应性方法实现了高质量的解决方案,仅使用传统方法所需的FEM评估的2.8%,没有替代模型.
- 在不同的MEMS设备和复杂的设计场景中,包括离散变量和严格的约束,证明了稳定性和多功能性.
结论:
- 拟议的自适应优化框架提高了MEMS设计中的准确性,速度和解决方案多样性.
- 在自适应框架内进行在线学习,与传统的线下优化技术相比,具有显著的优势.
- 该框架为优化MEMS设计的研究人员和从业人员提供了有价值,可靠和有效的资源.
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