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Many Objective Optimization of a Magnetic Micro–Electro–Mechanical (MEMS) Micromirror with Bounded MP-NSGA Algorithm

Author

Listed:
  • Paolo Di Barba

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy)

  • Maria Evelina Mognaschi

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy)

  • Elisabetta Sieni

    (Department of Theoretical and Applied Sciences–DiSTA, University of Insubria, via J.H. Dunant, 3, 21100 Varese, Italy)

Abstract

The paper proposes the automated optimal design of a class of micro–electro–mechanical (MEMS) devices, based on a procedure of finite element analysis coupled to evolutionary optimization algorithms. A magnetic MEMS, used as an optical switch, is considered as the case study. In particular, the geometry of the device is optimized in order to maximize the actuation torque and minimize the power losses and the device volume. The optimization algorithms belong to the genetic class and, in particular, Migrated Parents - Non-Dominated Sorting Genetic Algorithm MP-NSGA, with three objective functions, is compared to NSGA-III.

Suggested Citation

  • Paolo Di Barba & Maria Evelina Mognaschi & Elisabetta Sieni, 2020. "Many Objective Optimization of a Magnetic Micro–Electro–Mechanical (MEMS) Micromirror with Bounded MP-NSGA Algorithm," Mathematics, MDPI, vol. 8(9), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1509-:d:408971
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    References listed on IDEAS

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    1. Zhang, Yulong & Wang, Tianyang & Luo, Anxin & Hu, Yushen & Li, Xinxin & Wang, Fei, 2018. "Micro electrostatic energy harvester with both broad bandwidth and high normalized power density," Applied Energy, Elsevier, vol. 212(C), pages 362-371.
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