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.
No takes yet. Share an insight, caveat, or question.
Buhr et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: