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Producing Smart Pareto Sets for Multi-objective Topology Optimisation Problems

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Advances in Structural and Multidisciplinary Optimization (WCSMO 2017)

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Abstract

To date the design of structures via topology optimisation methods has mainly focused on single-objective problems. However, real-world design problems usually involve several different objectives, most of which counteract each other. Therefore, designers typically seek a set of Pareto optimal solutions, a solution for which no other solution is better in all objectives, which capture the trade-off between these objectives. This set is known as a smart Pareto set. Currently, only the weighted sums method has been used for generating Pareto fronts with topology optimisation methods. However, the weighted sums method is unable to produce evenly distributed smart Pareto sets. Furthermore, evenly distributed weights have been shown to not produce evenly spaced solutions. Therefore, the weighted sums method is not suitable for generating smart Pareto sets. Recently, the smart normal constraints method has been shown to be capable of directly generating smart Pareto sets. This work presents an updated smart normal constraint method, which is combined with a bi-directional evolutionary structural optimisation algorithm for multi-objective topology optimisation. The smart normal constraints method has been modified by further restricting the feasible design space for each optimisation run such that dominant and redundant points are not found. The algorithm is tested on several different structural optimisation problems. A number of different structural objectives are analysed, namely compliance, dynamic and buckling objectives. Therefore, the method is shown to be capable of solving various types of multi-objective structural optimisation problems. The goal of this work is to show that smart Pareto sets can be produced for complex topology optimisation problems. Furthermore, this research hopes to highlight the gap in the literature of topology optimisation for multi-objective problems.

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Author information

Authors and Affiliations

  1. The University of Sydney, Sydney, Australia

    David J. Munk, Gareth A. Vio & Grant P. Steven

  2. University of Cambridge, Cambridge, UK

    Timoleon Kipouros

Authors
  1. David J. Munk
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  2. Gareth A. Vio
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  3. Grant P. Steven
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  4. Timoleon Kipouros
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Corresponding author

Correspondence to David J. Munk .

Editor information

Editors and Affiliations

  1. Chair for Optimization of Mechanical Stuctures, Faculty for Mechanical Engineering and Safety Engineering, University of Wuppertal, Wuppertal, Germany

    Axel Schumacher

  2. Institute for Construction Enginnering, Technische Universität Braunschweig, Braunschweig, Germany

    Thomas Vietor

  3. Braunschweig, Germany

    Sierk Fiebig

  4. Chair of Structural Analysis, Technische University of Munich, Munich, Germany

    Kai-Uwe Bletzinger

  5. Engineering Center, ECAE 197, University of Colorado Boulder, Boulder, Colorado, USA

    Kurt Maute

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Munk, D.J., Vio, G.A., Steven, G.P., Kipouros, T. (2018). Producing Smart Pareto Sets for Multi-objective Topology Optimisation Problems. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-67988-4_10

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