A Surrogate-Assisted Cooperative Co-evolutionary Algorithm for Solving High Dimensional, Expensive and Black Box Optimization Problems

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Many research efforts have been recently focus to solve large-scale global optimization (LSGO) problems by means of evolutionary algorithms. Cooperative co-evolution has been proposed to solve such problems depending on thousands of variables. This methodology has proved very efficient in solving a wide range of LSGO problems. Nevertheless, it often requires an extremely large number of function evaluations to reach a suitable solution. This is somewhat problematic when the function evaluation is computationally expensive. A globally effective approach to high-fidelity optimization problems based on such expensive analyses lies in the exploitation of surrogate models. They act as cheap-to-evaluate alternatives to the original high-fidelity models reducing the computational cost, while still providing improved designs. This kind of optimization process, referred to as surrogate-assisted optimization, has proved very efficient on small-dimensional problems but suffers from the curse of dimensionality to solve LSGO problems. In this paper, cooperative co-evolution was combined with surrogate-assisted optimization in order to efficiently solve high dimensional, expensive and black-box problems. Experimental results are provided on a wide set of benchmark problems and show promising results for the proposed algorithm.
langue originaleAnglais
Pages (de - à)41-52
Nombre de pages12
journalEngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization
Les DOIs
étatPublié - 14 sept. 2018
EvénementEngOpt2018: 6th International Conference on Engineering Optimization - Instituto Superior Tecnico, Libsonne, Portugal
Durée: 17 sept. 201819 févr. 2019
http://engopt2018.tecnico.ulisboa.pt/

Empreinte digitale

Global optimization
Evolutionary algorithms
Function evaluation
Costs

mots-clés

  • Global optimization
  • Surrogate-assisted optimization
  • Large-scale optimization
  • High dimensional
  • Expensive and black-box functions
  • Cooperative co-evolutionary algorithm
  • Random grouping
  • Genetic algorithm
  • Evolutionary algorithm

Citer ceci

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title = "A Surrogate-Assisted Cooperative Co-evolutionary Algorithm for Solving High Dimensional, Expensive and Black Box Optimization Problems",
abstract = "Many research efforts have been recently focus to solve large-scale global optimization (LSGO) problems by means of evolutionaryalgorithms. Cooperative co-evolution has been proposed to solve suchproblems depending on thousands of variables. This methodology hasproved very efficient in solving a wide range of LSGO problems. Never-theless, it often requires an extremely large number of function evalua-tions to reach a suitable solution. This is somewhat problematic whenthe function evaluation is computationally expensive. A globally effectiveapproach to high-fidelity optimization problems based on such expensiveanalyses lies in the exploitation of surrogate models. They act as cheap-to-evaluate alternatives to the original high-fidelity models reducing thecomputational cost, while still providing improved designs. This kind ofoptimization process, referred to as surrogate-assisted optimization, hasproved very efficient on small-dimensional problems but suffers from thecurse of dimensionality to solve LSGO problems. In this paper, coop-erative co-evolution was combined with surrogate-assisted optimizationin order to efficiently solve high dimensional, expensive and black-boxproblems. Experimental results are provided on a wide set of benchmarkproblems and show promising results for the proposed algorithm.",
keywords = "Global optimization, Surrogate-assisted optimization, Large-scale optimization, High dimensional, Expensive and black-box functions, Cooperative co-evolutionary algorithm, Random grouping, Genetic algorithm, Evolutionary algorithm, global optimization, Surrogate-assisted optimization, large-scale optimization, high dimensional, Expensive and black-box functions, Cooperative co-evolutionary algorithm, Random grouping, genetic algorithm, Evolutionary algorithm",
author = "Julien Blanchard and Charlotte Beauthier and Timoteo Carletti",
year = "2018",
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doi = "https://doi.org/10.1007/978-3-319-97773-7_4",
language = "English",
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journal = "EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization",
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AU - Beauthier, Charlotte

AU - Carletti, Timoteo

PY - 2018/9/14

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N2 - Many research efforts have been recently focus to solve large-scale global optimization (LSGO) problems by means of evolutionaryalgorithms. Cooperative co-evolution has been proposed to solve suchproblems depending on thousands of variables. This methodology hasproved very efficient in solving a wide range of LSGO problems. Never-theless, it often requires an extremely large number of function evalua-tions to reach a suitable solution. This is somewhat problematic whenthe function evaluation is computationally expensive. A globally effectiveapproach to high-fidelity optimization problems based on such expensiveanalyses lies in the exploitation of surrogate models. They act as cheap-to-evaluate alternatives to the original high-fidelity models reducing thecomputational cost, while still providing improved designs. This kind ofoptimization process, referred to as surrogate-assisted optimization, hasproved very efficient on small-dimensional problems but suffers from thecurse of dimensionality to solve LSGO problems. In this paper, coop-erative co-evolution was combined with surrogate-assisted optimizationin order to efficiently solve high dimensional, expensive and black-boxproblems. Experimental results are provided on a wide set of benchmarkproblems and show promising results for the proposed algorithm.

AB - Many research efforts have been recently focus to solve large-scale global optimization (LSGO) problems by means of evolutionaryalgorithms. Cooperative co-evolution has been proposed to solve suchproblems depending on thousands of variables. This methodology hasproved very efficient in solving a wide range of LSGO problems. Never-theless, it often requires an extremely large number of function evalua-tions to reach a suitable solution. This is somewhat problematic whenthe function evaluation is computationally expensive. A globally effectiveapproach to high-fidelity optimization problems based on such expensiveanalyses lies in the exploitation of surrogate models. They act as cheap-to-evaluate alternatives to the original high-fidelity models reducing thecomputational cost, while still providing improved designs. This kind ofoptimization process, referred to as surrogate-assisted optimization, hasproved very efficient on small-dimensional problems but suffers from thecurse of dimensionality to solve LSGO problems. In this paper, coop-erative co-evolution was combined with surrogate-assisted optimizationin order to efficiently solve high dimensional, expensive and black-boxproblems. Experimental results are provided on a wide set of benchmarkproblems and show promising results for the proposed algorithm.

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KW - Expensive and black-box functions

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KW - Evolutionary algorithm

KW - global optimization

KW - Surrogate-assisted optimization

KW - large-scale optimization

KW - high dimensional

KW - Expensive and black-box functions

KW - Cooperative co-evolutionary algorithm

KW - Random grouping

KW - genetic algorithm

KW - Evolutionary algorithm

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