Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and oft...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 51; H. 8; S. 3925 - 3937
Hauptverfasser: Li, Jian-Yu, Zhan, Zhi-Hui, Wang, Hua, Zhang, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Buildings
Data models
Data-driven evolutionary algorithm (DDEA)
Design optimization
ensemble surrogates
Evolutionary algorithms
Evolutionary computation
genetic algorithm (GA)
Genetic algorithms
Iron
Neural networks
Optimization
Perturbation
Perturbation methods
Radial basis function
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses. For the experimental comparisons, a specific DDEA-PES algorithm is developed as an instance by adopting a genetic algorithm as the optimizer and radial basis function neural networks as the base models. The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs. Moreover, when compared with traditional nondata-driven methods, the proposed DDEA-PES algorithm only requires about 2% computational budgets to produce competitive results.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.3008280