Xi Zhang

East China University of Science and Technology

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Guiyun Zhang

China People's Police University

Feng Qian

East China University of Science and Technology

Yaochu Jin

Westlake University

Dong Zhang

Tianjin Chengjian University

Liping Zhang

Shanxi Agricultural University

Dong Zhang

City University of Hong Kong

Feng Qian

East China University of Science and Technology

Guo Yu

Xiangtan University

Liping Zhang

Lanzhou University of Technology

Liping Zhang

Harbin University of Science and Technology

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East China University of Science and Technology

Shanxi Agricultural University

Huazhong University of Science and Technology

Donghua University

Ministry of Education of the People's Republic of China

Tongji University

Wuhan University of Science and Technology

China People's Police University

Tianjin Normal University

China University of Mining and Technology

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Correction: Zhang et al. Dynamic Multi-Objective Optimization in Brazier-Type Gasification and Carbonization Furnace. Materials 2023, 16, 1164

Materials (2024)

Xi Zhang Guiyun Zhang Dong Zhang Liping Zhang

In consideration of the contributions to this work, Feng Qian unequivocally requests the removal of his name from the author list of this publication [...]

Zhàng

10.3390/ma17061233

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Citations (0)

Elitism-based transfer learning and diversity maintenance for dynamic multi-objective optimization

Information Sciences (2023)

Xi Zhang Guo Yu Yaochu Jin Feng Qian

Elitism

10.1016/j.ins.2023.04.006

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Citations (19)

Dynamic Multi-Objective Optimization in Brazier-Type Gasification and Carbonization Furnace

Materials (2023)

Xi Zhang Guiyun Zhang Dong Zhang Liping Zhang

With the special porous structure and super-long carbon sequestration characteristic, the biochar has shown to have potential in improving soil fertility, reducing carbon emissions and increasing soil carbon sequestration. However, the biochar technology has not been applied on a large scale, due to the complex structure, long transportation distance of raw materials, and high cost. To overcome these issues, the brazier-type gasification and carbonization furnace is designed to carry out dry distillation, anaerobic carbonization and have a high carbonization rate under high-temperature conditions. To improve the operation and maintenance efficiency, we formulate the operation of the brazier-type gasification and carbonization furnace as a dynamic multi-objective optimization problem (DMOP). Firstly, we analyze the dynamic factors in the work process of the brazier-type gasification and carbonization furnace, such as the equipment capacity, the operating conditions, and the biomass treated by the furnace. Afterward, we select the biochar yield and carbon monoxide emission as the dynamic objectives and model the DMOP. Finally, we apply three dynamic multiobjective evolutionary algorithms to solve the optimization problem so as to verify the effectiveness of the dynamic optimization approach in the gasification and carbonization furnace.

Carbon fibers

10.3390/ma16031164

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A self-adaptive dynamic multi-objective optimization algorithm based on transfer learning and elitism-based mutation

Neurocomputing (2023)

Xi Zhang Yaochu Jin Feng Qian

Elitism

Adaptive mutation

10.1016/j.neucom.2023.126761

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Cluster-Based Regression Transfer Learning for Dynamic Multi-Objective Optimization

Processes (2023)

Xi Zhang Feng Qian Liping Zhang

Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical information to guide the evolutionary search. However, there is still much room for improvement in the transfer effect and the computational efficiency. In this paper, we propose a cluster-based regression transfer learning-based dynamic multi-objective evolutionary algorithm named CRTL-DMOEA. It consists of two components, which are the cluster-based selection and cluster-based regression transfer. In particular, once a change occurs, we employ a cluster-based selection mechanism to partition the previous Pareto optimal solutions and find the clustering centroids, which are then fed into autoregression prediction model. Afterwards, to improve the prediction accuracy, we build a strong regression transfer model based on TrAdaboost.R2 by taking advantage of the clustering centroids. Finally, a high-quality initial population for the new environment is predicted with the regression transfer model. Through a comparison with some chosen state-of-the-art algorithms, the experimental results demonstrate that the proposed CRTL-DMOEA is capable of improving the performance of dynamic optimization on different test problems.

Centroid

Transfer of learning

10.3390/pr11020613

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Citations (3)