Publications
A novel Tchebycheff set scalarization approach to find a small set of solutions to tackle a large number of optimization objectives.
Xi Lin, Yilu Liu, Xiaoyuan Zhang, Fei Liu, Zhenkun Wang, and Qingfu Zhang
EoH combines LLMs and EC to automate the design of heuristics. EoH co-evolves “thoughts” (natural language as ideas) and executable code using LLMs, outperforming heuristics designed by human experts on various combinatorial optimization problems.
Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang
MOEA/D is a multiobjective evolutionary algorithm that uses decomposition to optimize subproblems simultaneously. MOEA/D is now one of the two most used frameworks for solving multiobjective optimization problems.
Qingfu Zhang and Hui Li
Improve practical utilities of neural architecture search through many-objective optimization, iterative surrogate modeling, and transfer learning.
Zhichao Lu , Gautam Sreekumar, Erik Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, and Vishnu N. Boddeti
Tutorials
[1] Multi-objective Algorithm Design using Large Language Models
Fei Liu, Zhichao Lu, Xi Lin, Qingfu Zhang, and Zhenkun Wang
EMO 25, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization 2025
[2] Multi-Objective Machine Learning
Vishnu Naresh Boddeti, Zhichao Lu, Xi Lin, Qingfu Zhang, and Kalyanmoy Deb
WCCI 24, Proceedings of the IEEE World Congress on Computational Intelligence 2024
[3] Multi-Objective Deep Learning
Vishnu Naresh Boddeti, Zhichao Lu, Xi Lin, Qingfu Zhang, and Kalyanmoy Deb
CVPR 23, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023
Surveys
[1] Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
Weiyu Chen, Xiaoyuan Zhang, Baijiong Lin, Xi Lin, Han Zhao, Qingfu Zhang, James T. Kwok
arXiv, 2025
[2] A Systematic Survey on Large Language Models for Algorithm Design
Fei Liu, Yiming Yao, Ping Guo, Zhiyuan Yang, Zhe Zhao, Xi Lin, Xialiang Tong, Mingxuan Yuan, Zhichao Lu, Zhenkun Wang, and Qingfu Zhang
arXiv, 2024
[3] Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
Fei Liu, Chengyu Lu, Lin Gui, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
arXiv, 2023
Software
[1] LLM4AD: A Platform for Algorithm Design with Large Language Model
Fei Liu, Rui Zhang, Zhuoliang Xie, Rui Sun, Kai Li, Xi Lin, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang
[2] LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
Xiaoyuan Zhang, Liang Zhao, Yingying Yu, Xi Lin, Yifan Chen, Han Zhao, Qingfu Zhang
Selected List of publications
Full list of papers are available on CityU Scholars.
Journal
[1] DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization
Cheng Gong, Yang Nan, Ke Shang, Ping Guo, Hisao Ishibuchi, and Qingfu Zhang
IEEE Transactions on Evolutionary Computation (TEVC), 2024
[2] Many-to-Few Decomposition: Linking R2-Based and Decomposition-Based Multiobjective Efficient Global Optimization Algorithms
Liang Zhao, Xiaobin Huang, Chao Qian, and Qingfu Zhang
IEEE Transactions on Evolutionary Computation (TEVC), 2024
[3] Machine Learning Assisted Multiobjective Evolutionary Algorithm for Routing and Packing
Fei Liu, Qingfu Zhang, Qingling Zhu, Tong Xialiang, and Mingxuan Yuan
IEEE Transactions on Evolutionary Computation (TEVC), 2024
[4] Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization
Liang Zhao and Qingfu Zhang
IEEE Transactions on Evolutionary Computation (TEVC), 2023
Conference
[1] Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, and Hau-San Wong
International Conference on Machine Learning (ICML), 2024
[2] Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, and Qingfu Zhang
International Conference on Machine Learning (ICML), 2024
[3] Two Fists, One Heart: Multi-Objective Optimization Based Strategy Fusion for Long-tailed Learning
Zhe Zhao, Pengkun Wang, HaiBin Wen, Wei Xu, Song Lai, Qingfu Zhang, and Yang Wang
International Conference on Machine Learning (ICML), 2024