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Preference-Based Multiobjective Particle Swarm Optimization for Airfoil Design

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

A significant challenge to the application of evolutionary multiobjective optimization (GlossaryTerm

EMO

) for transonic airfoil design is the often excessive number of computational fluid dynamic (GlossaryTerm

CFD

) simulations required to ensure convergence. In this study, a multiobjective particle swarm optimization (GlossaryTerm

MOPSO

) framework is introduced, which incorporates designer preferences to provide further guidance in the search. A reference point is projected onto the Pareto landscape by the designer to guide the swarm towards solutions of interest. The framework is applied to a typical transonic airfoil design scenario for robust aerodynamic performance. Time-adaptive Kriging models are constructed based on a high-fidelity Reynolds-averaged Navier–Stokes (GlossaryTerm

RANS

)solver to assess the performance of the solutions. The successful integration of these design tools is facilitated through the reference point, which ensures that the swarm does not deviate from the preferred search trajectory. A comprehensive discussion on the proposed optimization framework is provided, highlighting its viability for the intended design

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Abbreviations

CFD:

computational fluid dynamics

EA:

evolutionary algorithm

EMO:

evolutionary multiobjective optimization

FMG:

full multi-grid

LHS:

latin hypercube sampling

MOO:

multi-objective optimization

MOP:

multiobjective problem

MOPSO:

multiobjective particle swarm optimization

NASA:

National Aeronautics and Space Administration

NSGA:

nondominated sorting genetic algorithm

NSPSO:

nondominated sorting particle swarm optimization

PSO:

particle swarm optimization

RANS:

Reynolds-averaged Navier–Stokes

SOM:

self-organizing map

UPMOPSO:

user-preference multiobjective PSO

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Author information

Authors and Affiliations

  1. LEAP Australia Pty. Ltd., Clayton North, Australia

    Robert Carrese

  2. School of Computer Science and Information Technology, RMIT University, GPO Box 2476, 3001, Melbourne, Australia

    Xiaodong Li

Authors
  1. Robert Carrese
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  2. Xiaodong Li
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Corresponding author

Correspondence to Robert Carrese .

Editor information

Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Carrese, R., Li, X. (2015). Preference-Based Multiobjective Particle Swarm Optimization for Airfoil Design. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_67

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

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