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}
}