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Simulation-based optimization is a useful method for practical designproblems. However, it is difficult for complicated problems due toexpensive-computational costs. A popular way to overcome this issue is to use asurrogate model to save the cost. Nevertheless, limited design parameters thoseare input to traditional surrogate models can difficultly represent the wholedesign problem, which might result in unexpected errors. In this study,physical cloud images from simulations are employed and attempted to constructthe surrogate model. Simultaneously, based on the strong pattern recognitionand generation abilities of deep learning models, a novel Generative ReverseNet assisted Evolution Algorithm (GRN-EA) is proposed for expensive-designproblems. In this study, a numerical example of a Variable-Stiffness (VS)composite hole-plate is employed to obtain the optimal distribution of thecurved fiber. Moreover, to evaluate the performance of GRN-EA in practicalengineering problems, a more complex sheet-forming case is optimized. Accordingto the two problems, some expensive-simulations can be well handled in thisstudy.

A novel generative reverse net assisted evolution algorithm for expensive-computational optimizations