Associate Professor, Associate Head of Department Huazhong University of Science and Technology (HUST) IEEE Senior Member, chenghehust [at] gmail.com Dr. Cheng He is currently an Associate Professor with the School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, China. His main research interests are Artificial/Computational Intelligence (including evolutionary multi-objective optimization, model-based optimization, large-scale optimization, etc.).
We have categorized our articles as surrogate-assisted optimization, large-scale optimization, multi-/many-objective optimization, and deep learning and its applications. You are welcome to cite these papers via the Bibtex.
- 01/2025: Our paper titled "Surrogate-assisted Multiobjective Gene Selection for Cell Classification from Large-scale Single-cell RNA Sequencing Data" is accepted to IEEE Transactions on Evolutionary Computation! Thanks to PhD Candidate Jianqing Lin for his hard work on the paper! This work proposes a surrogate-assisted evolutionary algorithm for multiobjective Gene selection (GS). Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. Gene expression analysis results of selected genes validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data.
- 01/2025: Our paper titled ""Knowledge-assisted Approach for Contactless Current Measurement in Multiconductor Systems" is accepted to IEEE Transactions on Industry Applications! Thanks to PhD Candidate Chaojun Ma for his hard work on the paper! This work proposed a novel approach that combines a cost-effective annular magnetic field (MF) sensor array with an inverse calculation technique for precise, contactless current measurements in multiconductor systems. Experimental results demonstrate that the proposed approach achieves high accuracy in current measurement considering the impact of conductor positions, with error rates maintained below 1\% and 2\% in balanced and unbalanced cases, respectively. Additionally, an abnormal MF sensing data correction method is developed to ensure measurement accuracy further, showing resilience to sensor anomalies and maintaining a relative measurement error below 2\% after correction."
- 08/2024: Thanks for the interview invitation from Blue Tech Wave during the DOCS 2024. This interview is about the challenges and opportunities of large-scale multiobjective optimization, and the interview is listed for your reference.
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Cheng He , Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, Xing Yao IEEE Transactions on Evolutionary Computation, 2019 paper code poster LSMOF is an effective framework for large-scale multiobjective optimization. Generally, this framework can reduce the number of decision variables from 1000 to less than 20. The cost of FEs is less than 100,000 for conventional LSMOPs. |
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Cheng He , Shuhua Huang, Ran Cheng, Tan Kay Chen Yaochu Jin, IEEE Transactions on Cybernetics, 2021 paper code poster GMOEA focuses on efficient offspring generation via learning from the distribution of promising solutions. GMOEA is capable of handling MOPs with up to 200 decision variables effectively, which is a new research direction for model-based evolutionary computation. |
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Cheng He , Ran Cheng, Chuanji Zhang, Ye Tian, Qin Cheng Xin Yao, IEEE Transactions on Evolutionary Computation, 2020 paper code TREE is a large-scale multiobjective optimization test suite extracted from the power dilivery system, aiming at handling real-time ratio error estimation of voltage transformers. Generally, the maximum number of decision variables is up to half a million and it includes constraints, which provides a guidance for the desgin of meaningful evolutionary algorithm. |
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Linqiang Pan, Cheng He Ye Tian, Handing Wang, Xingyi Zhang, Yaochu Jin. IEEE Transactions on Evolutionary Computation, 2018 code CSEA is a classification-based surrogate-assisted evolutionary algorithm, which uses an uncertainty configuration to balance between convergence and uncertainty. |