# Supplementary Materials #

This website maintains the supplementary materials related to the following paper:

Ke Li, Guiyu Lai+, Xin Yao, “Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank”, IEEE Trans. Evol. Comput., accepted for publication, 2022.

It consists of the following parts:

- The source code of this paper can be found from our Github repo.
- Appendix document of this paper can be found from this Dropbox link.
- Video clips of the best policy obtained by different algorithms. In particular,
\(f_1\)
is the x-axis speed,
\(f_2\)
is the y-axis speed,
\(f_3 \text{ is the energy consumption} \)
- BC-EMO: \( (f_1=68.45730, f_2=111.72400, f_3=95.55980)^\top \)
- NEMO-0: \( (f_1=68.45730, f_2=111.72400, f_3=95.55980)^\top \)
- I-MOEA/D-PLVF: \( (f_1=156.62800, f_2=30.54620, f_3=75.83200)^\top \)
- IEMO/D: \( (f_1=66.09890, f_2=61.94570, f_3=75.91520)^\top \)
- I-NSGA-II/LTR: \( (f_1=100.52500, f_2=96.36920, f_3=100.34000)^\top \)
- I-MOEA/D/LTR: \( (f_1=114.31000, f_2=68.71040, f_3=102.49200)^\top \)
- I-R2-IBEA/LTR: \( (f_1=120.48400, f_2=74.36900, f_3=111.71900)^\top \)

Please cite the paper by using the following bibtex.

```
@article{LiLY22,
author = {Ke Li and
Guiyu Lai and
Xin Yao},
title = {Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank},
journal = {{IEEE} Trans. Evol. Comput.},
pages = {1--15},
year = {2022},
note = {accepted for publication}
}
```