R2/D-EGO

This repository contains the Matlab code of R2/D-EGO.

Liang Zhao, Xiaobin Huang, Chao Qian, and Qingfu Zhang. Many-to-Few Decomposition: Linking R2-based and Decomposition-based Multiobjective Efficient Global Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 29(5), 1873-1887, 2025. [Accepted Version] [PDF]

Usage

Matlab >= 2018a

Quick Start

• The run_synthetic.m provides the basic script to run experiments on ZDT and DTLZ.

Advanced usage

• Download PlatEMO (version >=4.6, Matlab >= 2018a).
• Copy the folders within "Algorithms" into the directory at "PlatEMO/Algorithms/". Next, add all of the subfolders contained within the "PlatEMO" directory to the MATLAB search path.
• In the MATLAB command window, type platemo() to run PlatEMO using the GUI.
• Select the label "expensive" and choose the algorithm "R2D-EGO".
  • Default setting of batch size: 5.
• Select a problem and set appropriate parameters.
  • e.g., ZDT1, N=200, M=2, D=8, maxFE=200.

If you have any questions or feedback, please feel free to contact liang.zhao@cityu.edu.hk and qingfu.zhang@cityu.edu.hk.

Citation

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

@article{zhao2025many,
  title={Many-to-few decomposition: Linking {R2}-based and decomposition-based multiobjective efficient global optimization algorithms},
  author={Zhao, Liang and Huang, Xiaobin and Qian, Chao and Zhang, Qingfu},
  journal={IEEE Transactions on Evolutionary Computation},
  year={2025},
  volume={29},
  number={5},
  pages={1873-1887},
  doi={10.1109/TEVC.2024.3434511}
  }

Acknowledgements

• This implementation is based on PlatEMO.
• For GP modeling, we leverage the DACE toolbox.