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An Efficient Hybrid Algorithm for Optimization of Discrete Structures

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Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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Abstract

Presented in this paper is a hybrid algorithm for the design of discrete structures like trusses. The proposed algorithm called Discrete Structures Optimization (DSO) is based on the Evolutionary Structural Optimization (ESO) [1,2]. In DSO, material is removed from the structural elements based on the strain energy. DSO is a two stage process. First stage is the topology optimization where the elements of the structure with the least amount of strain energy are identified and eliminated. The second stage is the sizing optimization of the structure with optimum topology identified in first stage. For the continuous design variables a gradient based method is used and for the discrete design variables a genetic algorithm is used. The algorithm is tested on 2-D and 3-D discrete structures. DSO results show significant reduction in the number of finite element analysis (FEA) evaluations as compared to genetic algorithms using simultaneous topology and sizing optimization.

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Author information

Authors and Affiliations

  1. School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australia

    Amitay Isaacs, Tapabrata Ray & Warren Smith

Authors
  1. Amitay Isaacs
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  2. Tapabrata Ray
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  3. Warren Smith
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Isaacs, A., Ray, T., Smith, W. (2008). An Efficient Hybrid Algorithm for Optimization of Discrete Structures. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_63

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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