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Multi-Objective Optimization for a Novel Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

[+] Author Affiliations
Yongquan Wang, Hualing Chen, Zhiying Ou, Xueming He

Xi’an Jiaotong University, Xi’an, China

Paper No. MNC2007-21169, pp. 21-26; 6 pages
  • 2007 First International Conference on Integration and Commercialization of Micro and Nanosystems
  • First International Conference on Integration and Commercialization of Micro and Nanosystems, Parts A and B
  • Sanya, Hainan, China, January 10–13, 2007
  • Conference Sponsors: Nanotechnology Institute
  • ISBN: 0-7918-4265-7 | eISBN: 0-7918-3794-7
  • Copyright © 2007 by ASME


In this paper, we present the multi-objective optimization for an entire microsystem, a novel capacitive electrostatic feedback accelerometer. From the energy relations of the coupled electrostatic-field, the dynamic model of the system is constructed. Aiming at the global performance, a multi-objective optimization model, where sensitivity, resolution and damping resonant frequency are selected as objectives, is established based on the concept of multidisciplinary design optimization (MDO). Genetic algorithm (GA) is used to solve this problem, and compared with a traditional optimization approach, sequence quadratic programming (SQP). Both the two algorithms can achieve our aim commendably, and the optimal solution given by GA is more satisfied. The research provides us a good foundation to develop the stochastic and implicit parallel properties of GA to obtain Pareto optimal solutions.

Copyright © 2007 by ASME



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