Expensive Multi-Objective of Pre-Oxidation Process Parameter Optimization Considering Heterogeneity

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@InProceedings{Tan:2023:ICAC,

• author = "Muyao Tan and Liang Jin and Kunlun Li",
• booktitle = "2023 28th International Conference on Automation and Computing (ICAC)",
• title = "Expensive Multi-Objective of Pre-Oxidation Process Parameter Optimization Considering Heterogeneity",
• year = "2023",
• abstract = "The precursor pre-oxidation process, as a critical link in carbon fiber production, stabilizes the properties of carbon fiber precursors, lays the foundation for the carbonization process, and is also a step that can directly determine the structural properties of carbon fiber, so it has been widely concerned by academic circles. Evaluating the density and strength of pre-oxidized filaments is expensive, time-consuming, and has different cost consumption, leading to heterogeneous problems in optimising pre-oxidized process parameters. An optimisation algorithm of pre-oxidation process parameters based on collaborative migration agent genetic programming is proposed to solve this problem. This method establishes a single-objective multi-precision surrogate model of the pre-oxidation process and a collaborative model between the two targets. The cooperative model predicts the high-cost target to reduce the operating cost in the calculation process. Our experimental results show that the proposed algorithm performs well in optimising pre-oxidation process parameters.",
• keywords = "genetic algorithms, genetic programming, Costs, Automation, Collaboration, Production, Predictive models, Prediction algorithms, heterogeneity, pre-oxidation process, multi-objective, parameter optimisation",
• DOI = "doi:10.1109/ICAC57885.2023.10275289",
• month = aug,
• notes = "Also known as \cite{10275289}",

}

Genetic Programming entries for Muyao Tan Liang Jin Kunlun Li

Citations