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Structural and Multidisciplinary Optimization

, Volume 57, Issue 2, pp 665–688 | Cite as

Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method

  • David J. Munk
  • Timoleon Kipouros
  • Gareth A. Vio
  • Geoffrey T. Parks
  • Grant P. Steven
RESEARCH PAPER

Abstract

To date the design of structures using topology optimization methods has mainly focused on single-objective problems. Since real-world design problems typically involve several different objectives, most of which counteract each other, it is desirable to present the designer with a set of Pareto optimal solutions that capture the trade-off between these objectives, known as a smart Pareto set. Thus far only the weighted sums and global criterion methods have been incorporated into topology optimization problems. Such methods are unable to produce evenly distributed smart Pareto sets. However, recently the smart normal constraint method has been shown to be capable of directly generating smart Pareto sets. Therefore, in the present work, an updated smart Normal Constraint Method is combined with a Bi-directional Evolutionary Structural Optimization (SNC-BESO) algorithm to produce smart Pareto sets for multiobjective topology optimization problems. Two examples are presented, showing that the Pareto solutions found by the SNC-BESO method make up a smart Pareto set. The first example, taken from the literature, shows the benefits of the SNC-BESO method. The second example is an industrial design problem for a micro fluidic mixer. Thus, the problem is multi-physics as well as multiobjective, highlighting the applicability of such methods to real-world problems. The results indicate that the method is capable of producing smart Pareto sets to industrial problems in an effective and efficient manner.

Keywords

Multiobjective optimization Multi-physics optimization Normal constraint method BESO Pareto 

Notes

Acknowledgements

D.J. Munk thanks the Australian government for their financial support through the Endeavour Fellowship scheme.

The authors would like to thank Dr Tiziano Ghisu for producing the data displayed in Fig. 14.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • David J. Munk
    • 1
  • Timoleon Kipouros
    • 2
  • Gareth A. Vio
    • 1
  • Geoffrey T. Parks
    • 2
  • Grant P. Steven
    • 1
  1. 1.The University of SydneySydneyAustralia
  2. 2.University of CambridgeCambridgeUK

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