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Evolutionary generation of neural network update signals for the topology optimization of structures

Published: 06 July 2013 Publication History

Abstract

In the adaptation of natural load bearing structures like bones and trees, regions subject to high physical loads accumulate structural material based on local stimuli, while it is reduced in others. This strategy can lead to efficient structures and has been modeled in the field of topology optimization. Instead of modeling the observed strategy we target the evolutionary process, which gave rise to theses strategies. We propose to use an evolutionary process in order to find a suitable mapping from local sensory information to an update signal, based on which a structure is adapted. The target is to evolve a generalizable update signal for quality functions that can not be optimized by existing topology optimization methods. As a first study, the update signal is represented by a feed-forward neural network model and its weights are tuned by an evolutionary strategy in order to optimize a minimum compliance structure. The resulting update signal is subsequently compared to the true compliance sensitivities and indicate that evolving a neural network update signal by optimization is a demanding task, yet possible at least for the provided example problem.

References

[1]
A. Baumgartner, L. Harzheim, and C. Mattheck. Sko (soft kill option): the biological way to find an optimum structure topology. International Journal of Fatigue, 14(6):387--393, 1992.
[2]
M. Bendsøe and O. Sigmund. Topology Optimization Theory, Methods and Applications. Springer Verlag Berlin, 2nd edition edition, 2004.
[3]
O. Sigmund. A 99 line topology optimization code written in matlab. Structural and Multidisciplinary Optimization, 21:120--127, 2001.
[4]
A. Tovar. Bone remodeling as a hybrid cellular automaton optimization process. PhD thesis, University of Notre Dame, 2004.

Cited By

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  • (2024)Deep learning in computational mechanics: a reviewComputational Mechanics10.1007/s00466-023-02434-474:2(281-331)Online publication date: 13-Jan-2024
  • (2023)Aligning optimization trajectories with diffusion models for constrained design generationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668380(51830-51861)Online publication date: 10-Dec-2023
  • (2022)On the use of artificial neural networks in topology optimisationStructural and Multidisciplinary Optimization10.1007/s00158-022-03347-165:10Online publication date: 1-Oct-2022
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Published In

cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. evolutionary learning
  2. evolutionary strategy
  3. neural network
  4. structural optimization
  5. topology optimization
  6. update signal

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Conference

GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2024)Deep learning in computational mechanics: a reviewComputational Mechanics10.1007/s00466-023-02434-474:2(281-331)Online publication date: 13-Jan-2024
  • (2023)Aligning optimization trajectories with diffusion models for constrained design generationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668380(51830-51861)Online publication date: 10-Dec-2023
  • (2022)On the use of artificial neural networks in topology optimisationStructural and Multidisciplinary Optimization10.1007/s00158-022-03347-165:10Online publication date: 1-Oct-2022
  • (2021)An intelligent algorithm for topology optimization in additive manufacturingThe International Journal of Advanced Manufacturing Technology10.1007/s00170-021-08014-1Online publication date: 12-Nov-2021
  • (2015)Neuro-evolutionary Topology Optimization with Adaptive Improvement ThresholdApplications of Evolutionary Computation10.1007/978-3-319-16549-3_53(655-666)Online publication date: 17-Mar-2015
  • (2014)Neuro-evolutionary topology optimization of structures by utilizing local state featuresProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598314(967-974)Online publication date: 12-Jul-2014

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