Abstract
In this study, Differential Evolution, a powerful metaheuristic algorithm, is employed to optimize the weight of truss structures. One of the major challenges of all metaheuristic algorithms is time-consuming where a large number of structural analyses are required. To deal with this problem, neural networks are used to quickly evaluate the response of the structures. Firstly, a number of data points are collected from a parametric finite element analysis, then the obtained datasets are used to train neural network models. Secondly, the trained models are utilized to predict the behavior of truss structures in the constraint handling step of the optimization procedure. Neural network models are developed using Python because this language supports many useful machine learning libraries such as scikit-learn, tensorflow, keras. Two well-known benchmark problems are optimized using the proposed approach to demonstrate its effectiveness. The results show that using neural networks helps to greatly reduce the computation time.
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This work was supported by the Domestic Ph.D. Scholarship Programme of Vingroup Innovation Foundation.
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Nguyen, TH., Vu, AT. (2020). Using Neural Networks as Surrogate Models in Differential Evolution Optimization of Truss Structures. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_12
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