A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization

In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different ty...

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Veröffentlicht in:Structural and multidisciplinary optimization Jg. 61; H. 2; S. 711 - 729
Hauptverfasser: Yu, Mingyuan, Li, Xia, Liang, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020
Springer Nature B.V
Schlagworte:
Computational Mathematics and Numerical Analysis
Computer simulation
Engineering
Engineering Design
Error analysis
Evolutionary algorithms
Genetic algorithms
Iterative methods
Optimization
Physical simulation
Reliability
Research Paper
Root-mean-square errors
Theoretical and Applied Mechanics
Expensive optimization
Adaptive surrogate model
Reliability
Evolutionary algorithm
ISSN:1615-147X, 1615-1488
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Zusammenfassung:In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with different types of expensive optimization problems. Therefore, how to select high-reliability surrogates to assist an evolutionary algorithm (EA) has always been a challenging task. This study aimed to dynamically provide an optimal surrogate for EA by developing a brand-new SAEA framework. Firstly, an adaptive surrogate model (ASM) selection technology was proposed. In ASM, according to different integration criteria from the strategy pool, elite meta-models were recombined into multiple ensemble surrogates in each iteration. Afterward, a promising model was adaptively picked out from the model pool based on the minimum root of mean square error (RMSE). Secondly, we investigated a novel ASM-based EA framework, namely ASMEA, where the reliability of all models was updated in real-time by generating new samples online. Thirdly, to verify the performance of the ASMEA framework, two instantiation algorithms are widely compared with several state-of-the-art algorithms on a commonly used benchmark test set. Finally, a real-world antenna structural optimization problem was solved by the proposed algorithms. The results demonstrate that the proposed framework is able to provide a high-reliability surrogate to assist EA in solving expensive optimization problems.
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-019-02391-8