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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.
Original language | English |
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Pages (from-to) | 41-52 |
Number of pages | 12 |
Journal | EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization |
DOIs | |
Publication status | Published - 14 Sept 2018 |
Event | EngOpt2018: 6th International Conference on Engineering Optimization - Instituto Superior Tecnico, Libsonne, Portugal Duration: 17 Sept 2018 → 19 Feb 2019 http://engopt2018.tecnico.ulisboa.pt/ |
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
Fingerprint
Dive into the research topics of 'A Surrogate-Assisted Cooperative Co-evolutionary Algorithm for Solving High Dimensional, Expensive and Black Box Optimization Problems'. Together they form a unique fingerprint.Projects
- 1 Finished
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CÉCI – Consortium of high performance computing centers
CHAMPAGNE, B., Lazzaroni, R., Geuzaine , C., Chatelain, P. & Knaepen, B.
1/01/18 → 31/12/22
Project: Research
Equipment
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High Performance Computing Technology Platform
Benoît Champagne (Manager)
Technological Platform High Performance ComputingFacility/equipment: Technological Platform
Student theses
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Challenging High Dimensionality in Evolutionary Optimization using Cooperative Co-evolutionary Algorithms
Author: Blanchard, J., 29 Jun 2021Supervisor: Carletti, T. (Supervisor), SARTENAER, A. (Jury), BEAUTHIER, C. (Jury), Mayer, A. (Jury), Tuyttens, D. (External person) (Jury) & El-Abd, M. (External person) (Jury)
Student thesis: Doc types › Doctor of Sciences
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