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Erschienen in:
2018 | OriginalPaper | Buchkapitel
An Artificial Neural Network-Based Optimization of Stiffened Composite Plate Using A New Adjusted Differential Evolution Algorithm
verfasst von : T. Lam-Phat, S. Nguyen-Hoai, V. Ho-Huu, Q. Nguyen, T. Nguyen-Thoi
Erschienen in: Proceedings of the International Conference on Advances in Computational Mechanics 2017
Verlag: Springer Singapore
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Abstract
Stiffened composite plates have been widely used in many engineering areas including construction, shipbuilding, and aircraft. And so, the demand of optimizing the design of stiffened composite plate has also been rising. In this paper, a so-called ABDE (ANN-based differential evolution) algorithm is introduced to search for the optimal design of stiffened composite plates. The new algorithm is the combination of the artificial neural network (ANN) and an improved differential evolution (DE) algorithm in solving optimization problems. In this technique, the ANN helps to quickly compute the respond of the structure, which is used in constraint handling step or finding the value of an objective function of DE algorithm. This helps to decrease the cost and increase the speed of convergence effectively.