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Abstract
Cooperative co-evolutionary algorithms have a huge potential in optimizing large-scale problems. In a such divide-and-conquer strategy, the decomposition step plays a crucial part in the performance of the algorithm. Automatic decomposition strategies that can uncover the interaction structure between decision variables have been introduced in recent years. However, such strategies for large-scale constrained problems are quite limited in number so far and yet, they are interesting for at least two reasons. On the one hand, they help to find a feasible region faster. On the other hand, they also improve the convergence rate for the optimization itself. In this paper, we propose a novel cooperative co-evolutionary algorithm, DGD-EA for Differential Grouping Evolutionary Algorithm, that performs an automatic decomposition of decision variables and allows to optimize large-scale constrained problems. Its performance is evaluated on a set of 10 benchmark functions specially created for this study.
Original language | English |
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Title of host publication | GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference |
Editors | Peter A. N. Bosman |
Publisher | ACM Press |
Pages | 697-704 |
Number of pages | 8 |
ISBN (Electronic) | 9781450349208 |
DOIs | |
Publication status | Published - 15 Jul 2017 |
Publication series
Name | GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference |
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Keywords
- global optimization
- large-scale optimization
- constrained optimization problems
- cooperative co-evolutionary algorythm
- differential grouping
- genetic algorithm
- Cooperative co-evolutionary algorithm
- Global optimization
- Genetic algorithm
- Constrained optimization problems
- Large-scale optimization
- Differential grouping
Fingerprint
Dive into the research topics of 'A Cooperative Co-evolutionary Algorithm for solving Large-Scale Constrained Problems with Interaction Detection'. Together they form a unique fingerprint.Projects
- 1 Finished
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CÉCI – Consortium of high performance computing centers
CHAMPAGNE, B. (PI), Lazzaroni, R. (PI), Geuzaine , C. (CoI), Chatelain, P. (CoI) & Knaepen, B. (CoI)
1/01/18 → 31/12/22
Project: Research
Equipment
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High Performance Computing Technology Platform
Champagne, B. (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
Blanchard, J. (Author), Carletti, T. (Supervisor), SARTENAER, A. (Jury), BEAUTHIER, C. (Jury), Mayer, A. (Jury), Tuyttens, D. (Jury) & El-Abd, M. (Jury), 29 Jun 2021Student thesis: Doc types › Doctor of Sciences
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