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Qingchao Jiang

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EAAI Journal 2025 Journal Article

Solving dynamic multi-objective optimization problem of immersed tunnel elements via multi-source evolutionary information clustering method

  • Qinqin Fan
  • Wentao Huang
  • Moduo Yu
  • Qirong Tang
  • Qingchao Jiang

Dynamic multi-objective optimization problems (DMOPs) are time- and space-varying, thus maintaining/improving the uncertainty degree of evolutionary information (i. e. , information entropy) in the population and providing useful knowledge are two important tasks to make dynamic multi-objective evolutionary algorithms (DMOEAs) adapt to changing environments. To achieve the above objectives, a multi-source population clustering (MPC) method is proposed to assist DMOEAs in improving their tracking performance during the full-cycle optimization in the current study. In the MPC, three different information sources are used to provide diverse spatiotemporal evolutionary information, aiding DMOEAs in adapting to various changing environments. Subsequently, an enhanced spectral clustering approach is employed to group all evolutionary individuals from different information sources into many clusters/subspaces. Finally, the selected DMOEA is employed to search all subspaces in parallel via the high-performing computing method. The MPC is incorporated into a regularity model-based multi-objective estimation of distribution algorithm (called as MPC-RM-MEDA) and is compared with six famous DMOEAs on 14 10- and 30-dimensional DMOPs, which are proposed in IEEE Congress on Evolutionary computation 2018. Experimental results demonstrate that the overall tracking performance of the proposed MPC-RM-MEDA is significantly superior to that of other selected competitors in various dynamic environments. Additionally, the MPC-RM-MEDA is utilized to address a real-world DMOP involving an immersed tunnel element. The obtained results and comparison with the knee point-based transfer learning method verify that the MPC is an efficient and dependable approach for enhancing the tracking performance of other DMOEAs in solving actual DMOPs.

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EAAI Journal 2024 Journal Article

Semi-supervised soft sensor method for fermentation processes based on physical monotonicity and variational autoencoders

  • Xinyue Cheng
  • Zhenhua Yu
  • Guan Wang
  • Qingchao Jiang
  • Zhixing Cao

Data-driven models have shown broad application prospects in soft sensor modeling. However, numerous challenges persist. On the one hand, data-driven soft sensor methods have high requirements on data quality. On the other hand, models relying on limited experimental data often lack physical interpretability. To tackle these challenges, a semi-supervised soft sensor method (PMVAER) for fermentation processes based on physical monotonicity and variational autoencoders (VAEs) is introduced. First, physical monotonicity constraint is incorporated into the loss function of VAEs for regression to ensure that the model's predictions adhere to physical feasibility. Next, considering the disparate sampling frequencies for process and quality variables, this approach is extended to learn from unlabeled data, creating a semi-supervised soft sensor model. The proposed model is validated on simulation and real cases of penicillin fermentation. Comparisons with five other methods verify that the proposed method exhibits exceptional predictive accuracy along with enhanced generalization ability.

Details DOI
v2026.09.13