Automatic synthesis of MEMS devices using self-adaptive hybrid metaheuristics

Cem Celal Tutum, Zhun Fan

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This paper introduces a multi-objective optimization ap- proach for layout synthesis of MEMS components. A case study of layout synthesis of a comb-driven micro-resonator shows that the approach proposed in this paper can lead to design results accommodating two design objectives, i.e. si- multaneous minimization of size and power input of a MEMS device, while investigating optimum geometrical conguration as the main concern. The major contribution of this paper is the application of self-adaptive memetic computing in MEMS design. An evolutionary multi-objective optimization (EMO) technique, in particular non-dominated sorting genetic algorithm (NSGA-II), has been applied to- gether with a pattern recognition statistical tool, i.e. Principal Component Analysis (PCA), to nd multiple trade-o solutions in an ecient manner. Following this, a gradient- based local search, i.e. sequential quadratic programming (SQP), is applied to improve and speed up the convergence of the obtained Pareto-optimal front. In order to reduce the number of function evaluations in the local search procedure, the obtained non-dominated solutions are clustered in the objective space and consequently, a post-optimality study is manually performed to nd out some common design principles among those solutions. Finally, two reasonable design choices have been oered based on manufacturability issues.
Original languageEnglish
Title of host publicationGECCO 2011
Publication date2011
ISBN (Print)978-1-4503-0690-4
Publication statusPublished - 2011
EventProceedings of the 13th annual conference companion on Genetic and Evolutionary Computation : Late Breaking Abstracts - Dublin, Ireland
Duration: 1 Jan 2011 → …


ConferenceProceedings of the 13th annual conference companion on Genetic and Evolutionary Computation : Late Breaking Abstracts
CityDublin, Ireland
Period01/01/2011 → …


  • Principal component analysis
  • Evolutionary multi-objective optimization
  • Local search
  • MEMS design

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