Tianxinhuang / DRS

Codes for Deep Residual Surrogate Model

DRS

The codes for Deep Residual Surrogate Model published in INFORMATION SCIENCES.

Environment

• Python 3.6.9
• numpy 1.14.5
• SMTorg package
• Touchstone package

Dataset

The Test problems are intergrated in the tf_util, which mainly come from Ackley, SMT and Touchstone.

Usage

1. Optimize different models

Python3 test_surro.py

Different models will be optimized and tested on the benmark problems required in file functions2.txt, the results will be saved in file result.xls.

2. Evaluate the performances

Python3 draw_pics.py

The performances could be compared through histograms.

Citation

If you find our work useful for your research, please cite:

@article{huang2022deep,
  title={Deep residual surrogate model},
  author={Huang, Tianxin and Liu, Yong and Pan, Zaisheng},
  journal={Information Sciences},
  volume={605},
  pages={86--98},
  year={2022},
  publisher={Elsevier}
}