Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models

Liu Chu; Eduardo Souza De Cursi; Abdelkhalak El Hami; Mohamed Eid

doi:doi:10.11648/j.acm.20150406.20

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Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models

Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid

Published in Applied and Computational Mathematics (Volume 4, Issue 6)

Received: 20 November 2015 Accepted: 29 November 2015 Published: 18 December 2015

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Abstract

Reliability based optimization (RBO) is one of the most appropriate methods for structural design under uncertainties. It searches for the best compromise between cost and safety while considering system uncertainties by incorporating reliability measures within the optimization. Despite the advantages of RBO, its application to practical engineering problem is still quite challenging. In this paper, we propose an effective method to decouple the loops of reliability assessment analysis and optimization by creating surrogate models. The Latin Hypercube sampling approach is applied to a structural finite element model to obtain an effective database for building surrogate models. In order to avoid premature convergence of the optimization process, the RBO problem is solved with metaheuristic methods such as genetic algorithm and simulated annealing. The relative efficiency of surrogate models and their relationship with metaheuristic search engine are discussed in the article.

Published in	Applied and Computational Mathematics (Volume 4, Issue 6)
DOI	10.11648/j.acm.20150406.20
Page(s)	462-468
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Topology and Sizing Optimization of Trusses, Gravitational Search Algorithm, Efficient Member Grouping, Double and Triple Layer Grid Structures

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APA Style

Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid. (2015). Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models. Applied and Computational Mathematics, 4(6), 462-468. https://doi.org/10.11648/j.acm.20150406.20

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ACS Style

Liu Chu; Eduardo Souza De Cursi; Abdelkhalak El Hami; Mohamed Eid. Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models. Appl. Comput. Math. 2015, 4(6), 462-468. doi: 10.11648/j.acm.20150406.20

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AMA Style

Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid. Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models. Appl Comput Math. 2015;4(6):462-468. doi: 10.11648/j.acm.20150406.20

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@article{10.11648/j.acm.20150406.20,
  author = {Liu Chu and Eduardo Souza De Cursi and Abdelkhalak El Hami and Mohamed Eid},
  title = {Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {6},
  pages = {462-468},
  doi = {10.11648/j.acm.20150406.20},
  url = {https://doi.org/10.11648/j.acm.20150406.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20150406.20},
  abstract = {Reliability based optimization (RBO) is one of the most appropriate methods for structural design under uncertainties. It searches for the best compromise between cost and safety while considering system uncertainties by incorporating reliability measures within the optimization. Despite the advantages of RBO, its application to practical engineering problem is still quite challenging. In this paper, we propose an effective method to decouple the loops of reliability assessment analysis and optimization by creating surrogate models. The Latin Hypercube sampling approach is applied to a structural finite element model to obtain an effective database for building surrogate models. In order to avoid premature convergence of the optimization process, the RBO problem is solved with metaheuristic methods such as genetic algorithm and simulated annealing. The relative efficiency of surrogate models and their relationship with metaheuristic search engine are discussed in the article.},
 year = {2015}
}

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TY  - JOUR
T1  - Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models
AU  - Liu Chu
AU  - Eduardo Souza De Cursi
AU  - Abdelkhalak El Hami
AU  - Mohamed Eid
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DO  - 10.11648/j.acm.20150406.20
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 462
EP  - 468
PB  - Science Publishing Group
SN  - 2328-5613
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AB  - Reliability based optimization (RBO) is one of the most appropriate methods for structural design under uncertainties. It searches for the best compromise between cost and safety while considering system uncertainties by incorporating reliability measures within the optimization. Despite the advantages of RBO, its application to practical engineering problem is still quite challenging. In this paper, we propose an effective method to decouple the loops of reliability assessment analysis and optimization by creating surrogate models. The Latin Hypercube sampling approach is applied to a structural finite element model to obtain an effective database for building surrogate models. In order to avoid premature convergence of the optimization process, the RBO problem is solved with metaheuristic methods such as genetic algorithm and simulated annealing. The relative efficiency of surrogate models and their relationship with metaheuristic search engine are discussed in the article.
VL  - 4
IS  - 6
ER  -

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Author Information

Liu Chu

Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, France
Eduardo Souza De Cursi

Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, France
Abdelkhalak El Hami

Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, France
Mohamed Eid

Laboratory of Optimization and Reliability in Mechanical Structure, Department of Mechanics, National Institute of Applied Science of Rouen, France

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APA Style

Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid. (2015). Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models. Applied and Computational Mathematics, 4(6), 462-468. https://doi.org/10.11648/j.acm.20150406.20

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ACS Style

Liu Chu; Eduardo Souza De Cursi; Abdelkhalak El Hami; Mohamed Eid. Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models. Appl. Comput. Math. 2015, 4(6), 462-468. doi: 10.11648/j.acm.20150406.20

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AMA Style

Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid. Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models. Appl Comput Math. 2015;4(6):462-468. doi: 10.11648/j.acm.20150406.20

Copy | Download

@article{10.11648/j.acm.20150406.20,
  author = {Liu Chu and Eduardo Souza De Cursi and Abdelkhalak El Hami and Mohamed Eid},
  title = {Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {6},
  pages = {462-468},
  doi = {10.11648/j.acm.20150406.20},
  url = {https://doi.org/10.11648/j.acm.20150406.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20150406.20},
  abstract = {Reliability based optimization (RBO) is one of the most appropriate methods for structural design under uncertainties. It searches for the best compromise between cost and safety while considering system uncertainties by incorporating reliability measures within the optimization. Despite the advantages of RBO, its application to practical engineering problem is still quite challenging. In this paper, we propose an effective method to decouple the loops of reliability assessment analysis and optimization by creating surrogate models. The Latin Hypercube sampling approach is applied to a structural finite element model to obtain an effective database for building surrogate models. In order to avoid premature convergence of the optimization process, the RBO problem is solved with metaheuristic methods such as genetic algorithm and simulated annealing. The relative efficiency of surrogate models and their relationship with metaheuristic search engine are discussed in the article.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Reliability Based Optimization with Metaheuristic Algorithms and Latin Hypercube Sampling Based Surrogate Models
AU  - Liu Chu
AU  - Eduardo Souza De Cursi
AU  - Abdelkhalak El Hami
AU  - Mohamed Eid
Y1  - 2015/12/18
PY  - 2015
N1  - https://doi.org/10.11648/j.acm.20150406.20
DO  - 10.11648/j.acm.20150406.20
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 462
EP  - 468
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20150406.20
AB  - Reliability based optimization (RBO) is one of the most appropriate methods for structural design under uncertainties. It searches for the best compromise between cost and safety while considering system uncertainties by incorporating reliability measures within the optimization. Despite the advantages of RBO, its application to practical engineering problem is still quite challenging. In this paper, we propose an effective method to decouple the loops of reliability assessment analysis and optimization by creating surrogate models. The Latin Hypercube sampling approach is applied to a structural finite element model to obtain an effective database for building surrogate models. In order to avoid premature convergence of the optimization process, the RBO problem is solved with metaheuristic methods such as genetic algorithm and simulated annealing. The relative efficiency of surrogate models and their relationship with metaheuristic search engine are discussed in the article.
VL  - 4
IS  - 6
ER  -

Copy | Download