The codes and data for the paper 'Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution' published in the journal named 'The International Journal of Advanced Manufacturing Technology'. The paper can be found at: https://doi.org/10.1007/s00170-023-12595-4
The codes contain both the proposed FCRN-RDE and an implementation of several evolution algorithms. Some codes are converted from C++ programs so that parts of them may look a little bit strange.
The studied problem belongs to a complex scheduling problem, and the proposed method is a type of surrogate-assisted evolutionary algorithm that can accelerate the speed of fitness evaluation in evolutionary algorithms. The codes contain six deep-learning surrogate models, including DNN, RNN, RCNN, TRANSFORMER, FCRN, and CNN.
To run the program, just execute:
python main.pyThe codes are simple but may be really effective in solving some large-scale optimization problems.
If you find something that may be helpful for your research, please cite our work as:
@article{wang2023large, title={Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution}, author={Wang, Xiaohan and Zhang, Lin and Laili, Yuanjun and Liu, Yongkui and Li, Feng and Chen, Zhen and Zhao, Chun}, journal={The International Journal of Advanced Manufacturing Technology}, pages={1--19}, year={2023}, publisher={Springer} }