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On-line Metamodel-Assisted Optimization with Mixed Variables

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Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 39))

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Abstract

The optimization of complex civil engineering structures remains a major scientific challenge, mostly because of the high number of calls to the finite element analysis required by the complete design process. To achieve a significant reduction of this computational effort, a popular approach consists in substituting the high-fidelity simulation by a lower-fidelity regression model, also called a metamodel. However, most metamodels (like kriging, radial basis functions, etc.) focus on continuous variables, thereby neglecting the large amount of problems characterized by discrete, integer, or categorical data. Therefore, in this chapter, a complete metamodel-assisted optimization procedure is proposed to deal with mixed variables. The methodology includes a multi-objective evolutionary algorithm and a multiple kernel regression model, both adapted to mixed data, as well as an efficient on-line enrichment of the metamodel during the optimization. A structural benchmark test case illustrates the proposed approach, followed by a critical discussion about the generalization of the concepts introduced in this chapter for metamodel-assisted optimization.

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Acknowledgments

This work has been supported by Innoviris (Brussels-Capital Region, Belgium) through a BB2B project entitled “Multicriteria optimization with uncertainty quantification applied to the building industry”.

The authors also acknowledge support by the Basic Project Foundation of Northwestern Polytechnical University (JC20120241), and by the National Natural Science Foundation of China (Grants No. 11302173, 51275424, and 11432011).

Author information

Authors and Affiliations

  1. Building, Architecture & Town Planning (BATir) Department, Brussels School of Engineering, Université libre de Bruxelles (ULB), Avenue F.D. Roosevelt, 50 (CP 194/2), 1050, Brussels, Belgium

    Rajan Filomeno Coelho

  2. Department of Civil and Environmental Engineering, Skempton Building, South Kensington Campus Imperial College London, SW7 2AZ, London, UK

    Manuel Herrera

  3. Department of Applied Mathematics, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an, Shaanxi, 710072, People’s Republic of China

    Manyu Xiao

  4. Engineering Simulation and Aerospace Computing, School of Mechanical Engineering, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an, Shaanxi, 710072, People’s Republic of China

    Weihong Zhang

Authors
  1. Rajan Filomeno Coelho
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  2. Manuel Herrera
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  3. Manyu Xiao
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  4. Weihong Zhang
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Corresponding author

Correspondence to Rajan Filomeno Coelho .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia civil, ISEP, Instituto Politecnico Porto, Porto, Portugal

    Jorge Magalhães-Mendes

  2. Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria—SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    David Greiner

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Filomeno Coelho, R., Herrera, M., Xiao, M., Zhang, W. (2015). On-line Metamodel-Assisted Optimization with Mixed Variables. In: Magalhães-Mendes, J., Greiner, D. (eds) Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management. Computational Methods in Applied Sciences, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-20406-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-20406-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20405-5

  • Online ISBN: 978-3-319-20406-2

  • eBook Packages: EngineeringEngineering (R0)

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