An asynchronous metamodel-assisted memetic algorithm for CFD-based shape optimization

This article presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for use on multiprocessor platforms and may solve computationally expensive optimization problems in reduced wall-clock time, compared to conv...

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Published inEngineering optimization Vol. 44; no. 2; pp. 157 - 173
Main Authors Kontoleontos, Evgenia A., Asouti, Varvara G., Giannakoglou, Kyriakos C.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.02.2012
Taylor & Francis Ltd
Subjects
adjoint method
Adjoints
Algorithms
asynchronous metamodel-assisted evolutionary algorithm
Computation
computational fluid dynamics
Engineering Sciences
Evolutionary algorithms
Fluid dynamics
Machinery
Mathematical analysis
Mathematical models
memetic algorithm
Optimization
Optimization algorithms
Shape optimization
Physical Sciences
Online AccessGet full text
ISSN0305-215X
1026-745X
1029-0273
1029-0273
DOI10.1080/0305215X.2011.570758

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Abstract This article presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for use on multiprocessor platforms and may solve computationally expensive optimization problems in reduced wall-clock time, compared to conventional evolutionary or memetic algorithms. It is, in fact, a hybridization of non-generation-based (asynchronous) evolutionary algorithms, assisted by surrogate evaluation models, a local search method and the Lamarckian learning process. For the objective function gradient computation, in CFD applications, the adjoint method is used. Issues concerning the 'smart' implementation of local search in multi-objective problems are discussed. In this respect, an algorithmic scheme for reducing the number of calls to the adjoint equations to just one, irrespective of the number of objectives, is proposed. The algorithm is applied to the CFD-based shape optimization of the tubes of a heat exchanger and of a turbomachinery cascade.
AbstractList This article presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for use on multiprocessor platforms and may solve computationally expensive optimization problems in reduced wall-clock time, compared to conventional evolutionary or memetic algorithms. It is, in fact, a hybridization of non-generation-based (asynchronous) evolutionary algorithms, assisted by surrogate evaluation models, a local search method and the Lamarckian learning process. For the objective function gradient computation, in CFD applications, the adjoint method is used. Issues concerning the 'smart' implementation of local search in multi-objective problems are discussed. In this respect, an algorithmic scheme for reducing the number of calls to the adjoint equations to just one, irrespective of the number of objectives, is proposed. The algorithm is applied to the CFD-based shape optimization of the tubes of a heat exchanger and of a turbomachinery cascade. [PUBLICATION ABSTRACT]
This paper presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for use on multiprocessor platforms and may solve computationally expensive optimization problems in reduced wall-clock time, compared to conventional evolutionary or memetic algorithms. It is, in fact, a hybridization of non-generation-based (asynchronous) evolutionary algorithms, assisted by surrogate evaluation models, a local search method and the Lamarckian learning process. For the objective functions gradient computation, in CFD applications, the adjoint method is used. Issues concerning the 'smart' implementation of local search in multi-objective problems are discussed. In this respect, an algorithmic scheme for reducing the number of calls to the adjoint equations to just one, irrespective of the number of objectives, is proposed. The algorithm is applied to CFD-based shape optimization of the tubes of a heat exchanger and of a turbomachinery cascade.
This article presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for use on multiprocessor platforms and may solve computationally expensive optimization problems in reduced wall-clock time, compared to conventional evolutionary or memetic algorithms. It is, in fact, a hybridization of non-generation-based (asynchronous) evolutionary algorithms, assisted by surrogate evaluation models, a local search method and the Lamarckian learning process. For the objective function gradient computation, in CFD applications, the adjoint method is used. Issues concerning the 'smart' implementation of local search in multi-objective problems are discussed. In this respect, an algorithmic scheme for reducing the number of calls to the adjoint equations to just one, irrespective of the number of objectives, is proposed. The algorithm is applied to the CFD-based shape optimization of the tubes of a heat exchanger and of a turbomachinery cascade.
Author Giannakoglou, Kyriakos C.
Kontoleontos, Evgenia A.
Asouti, Varvara G.
Author_xml – sequence: 1
  givenname: Evgenia A.
  surname: Kontoleontos
  fullname: Kontoleontos, Evgenia A.
  organization: Parallel CFD & Optimization Unit, School of Mechanical Engineering , National Technical University of Athens
– sequence: 2
  givenname: Varvara G.
  surname: Asouti
  fullname: Asouti, Varvara G.
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  givenname: Kyriakos C.
  surname: Giannakoglou
  fullname: Giannakoglou, Kyriakos C.
  email: kgianna@central.ntua.gr
  organization: Parallel CFD & Optimization Unit, School of Mechanical Engineering , National Technical University of Athens
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Snippet This article presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate...
This paper presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for...
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SubjectTerms adjoint method
Adjoints
Algorithms
asynchronous metamodel-assisted evolutionary algorithm
Computation
computational fluid dynamics
Engineering Sciences
Evolutionary algorithms
Fluid dynamics
Machinery
Mathematical analysis
Mathematical models
memetic algorithm
Optimization
Optimization algorithms
Shape optimization
Title An asynchronous metamodel-assisted memetic algorithm for CFD-based shape optimization
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