PSL-MOBO

Code for NeurIPS2022 Paper: Pareto Set Learning for Expensive Multi-Objective Optimization

The code is mainly designed to be simple and readable, it contains:

• run.py is a ~200-line main file to run the Pareto Set Learning (PSL) algorithm for MOBO;
• model.py is a simple FC Pareto Set model definition;
• function.py contains all the test problems used in the paper;
• lhs.py is an efficient latin-hypercube design implementation, which is for generating initial solutions;
• The folder mobo contains the files for surrogate model definition and training, which is borrowed from the DGEMO repository.

Reference

If you find our work is helpful to your research, please cite our paper:

@inproceedings{linpareto,
  title={Pareto Set Learning for Expensive Multi-Objective Optimization},
  author={Lin, Xi and Yang, Zhiyuan and Zhang, Xiaoyuan and Zhang, Qingfu},
  booktitle={Advances in Neural Information Processing Systems},
  volume={35},
  year={2022}
}