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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
J. Zhu, Z. Jiang, D. Pan, H. Yu, C. Xu, K. Zhou, and W. Gui, “A hybrid encoding-based coordinated optimization method for charging matrix design in the blast furnace ironmaking process,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2025.126011
Citation: J. Zhu, Z. Jiang, D. Pan, H. Yu, C. Xu, K. Zhou, and W. Gui, “A hybrid encoding-based coordinated optimization method for charging matrix design in the blast furnace ironmaking process,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2025.126011
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A Hybrid Encoding-Based Coordinated Optimization Method for Charging Matrix Design in the Blast Furnace Ironmaking Process

doi: 10.1109/JAS.2025.126011
Funds:  This work was supported in part by the Science and Technology Innovation Program of Hunan Province (2024RC1007), the Key Applied Basic Research Project of Baowu Group (F24YYLT30Z), the Young Scientists Fund of the National Natural Science Foundation of China (62303491), and Hunan Provincial Natural Science Foundation (2025JJ10007)
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    Abstract
  • A well-designed charging matrix (CM) is crucial for advancing green and low-carbon production in the blast furnace (BF) ironmaking process. Over recent years, metaheuristics algorithms have been applied to optimize CM, partially reducing reliance on on-site workers. However, CM optimization is a challenging mixed-variable constraint optimization problem. Prior studies predominantly simplify CM to either continuous or discrete forms via variable fixation or type conversion, which hinders the efficient joint optimization of heterogeneous variables, limiting optimization accuracy and search efficiency. To tackle this barrier, this study proposes a novel method named Hybrid Encoding-based Adaptive Coordinated Differential Evolution (HE-ACoDE), marking the first attempt to optimize CM from a mixed-variable perspective. First, a hybrid encoding scheme is devised to provide a unified representation for the mixed variables in CM. Then, a coordinated mixed-variable mutation strategy is developed, effectively facilitating the synchronized evolution of continuous and discrete variables. Moreover, a constraint-aware selection operator and a weight-guided parameter adaptation strategy are proposed, which collaboratively guide the population toward feasible, high-quality solutions across different evolutionary stages and problem landscapes. Extensive comparison experiments on two industrial scenarios demonstrate that HE-ACoDE outperforms state-of-the-art CM optimization and mixed-variable optimization methods in terms of accuracy, stability, and convergence performance.

     

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