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Neuro-evolutionary topology optimization of structures by utilizing local state features

Authors:

Markus OlhoferAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 967 - 974

https://doi.org/10.1145/2576768.2598314

Published: 12 July 2014 Publication History

Abstract

In this paper we propose a novel method for the topology optimization of mechanical structures, based on a hybrid combination of a neuro-evolution with a gradient-based optimizer. Conventional gradient-based topology optimization requires problem-specific sensitivity information, however this is not available in the general case. The proposed method substitutes the analytical gradient by an artificial neural network approximation model, whose parameters are learned by an evolutionary algorithm. Advantageous is that the number of parameters in the evolutionary search is not directly coupled to the mesh of the discretized design, potentially enabling the optimization of fine discretizations. Concretely, the network maps features, obtained for each element of the discretized design, to an update signal, that is used to determine a new design. A new network is learned for every iteration of the topology optimization. The proposed method is evaluated on the minimum compliance design problem, with two different sets of features. Feasible designs are obtained, showing that the neural network is able to successfully replace analytical sensitivity information. In concluding remarks, we discuss the significant improvement that is achieved when including the strain energy as feature.

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Cited By

Islam MLiu L(2022)Deep learning accelerated topology optimization with inherent control of image qualityStructural and Multidisciplinary Optimization10.1007/s00158-022-03433-465:11Online publication date: 2-Nov-2022
https://doi.org/10.1007/s00158-022-03433-4
Wang ZZhang YBernard A(2021)A constructive solid geometry-based generative design method for additive manufacturingAdditive Manufacturing10.1016/j.addma.2021.10195241(101952)Online publication date: May-2021
https://doi.org/10.1016/j.addma.2021.101952
Karimzadeh RHamedi M(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
https://doi.org/10.1007/s00170-021-08014-1
Show More Cited By

Index Terms

Neuro-evolutionary topology optimization of structures by utilizing local state features
1. Applied computing
  1. Arts and humanities
    1. Architecture (buildings)
      1. Computer-aided design
  2. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning

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Published In

cover image ACM Conferences

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 12 July 2014

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

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Cited By

Islam MLiu L(2022)Deep learning accelerated topology optimization with inherent control of image qualityStructural and Multidisciplinary Optimization10.1007/s00158-022-03433-465:11Online publication date: 2-Nov-2022
https://doi.org/10.1007/s00158-022-03433-4
Wang ZZhang YBernard A(2021)A constructive solid geometry-based generative design method for additive manufacturingAdditive Manufacturing10.1016/j.addma.2021.10195241(101952)Online publication date: May-2021
https://doi.org/10.1016/j.addma.2021.101952
Karimzadeh RHamedi M(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
https://doi.org/10.1007/s00170-021-08014-1
Guirguis DAulig NPicelli RZhu BZhou YVicente WIorio FOlhofer MMatusiks WCoello Coello CSaitou K(2020)Evolutionary Black-Box Topology Optimization: Challenges and PromisesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.295441124:4(613-633)Online publication date: Aug-2020
https://doi.org/10.1109/TEVC.2019.2954411
Rostami PMarzbanrad J(2020)Cooperative coevolutionary topology optimization using moving morphable componentsEngineering Optimization10.1080/0305215X.2020.1759579(1-22)Online publication date: 19-May-2020
https://doi.org/10.1080/0305215X.2020.1759579
Kentli A(2019)Topology Optimization Applications on Engineering StructuresTruss and Frames - Recent Advances and New Perspectives [Working Title]10.5772/intechopen.90474Online publication date: 16-Dec-2019
https://doi.org/10.5772/intechopen.90474
Yu YHur TJung JJang I(2019)Deep learning for determining a near-optimal topological design without any iterationStructural and Multidisciplinary Optimization10.1007/s00158-018-2101-559:3(787-799)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s00158-018-2101-5
Wilson KRostami S(2018)On the Integrity of Performance Comparison for Evolutionary Multi-objective Optimisation AlgorithmsAdvances in Computational Intelligence Systems10.1007/978-3-319-97982-3_1(3-15)Online publication date: 11-Aug-2018
https://doi.org/10.1007/978-3-319-97982-3_1
Bujny MAulig NOlhofer MDuddeck F(2016)Hybrid evolutionary approach for level set topology optimization2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7748335(5092-5099)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7748335
Aulig NOlhofer M(2016)Evolutionary computation for topology optimization of mechanical structures: An overview of representations2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744026(1948-1955)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7744026
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