炼钢
过程(计算)
水准点(测量)
分布估计算法
重采样
数学优化
人口
航程(航空)
计算机科学
概率逻辑
统计过程控制
质量(理念)
算法
工程类
数学
人工智能
地理
社会学
材料科学
冶金
人口学
航空航天工程
哲学
操作系统
认识论
大地测量学
作者
Lixin Tang,Chang Liu,Jiyin Liu,Xianpeng Wang
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-17
被引量:1
标识
DOI:10.1109/tsmc.2019.2962880
摘要
This article studies an operation optimization problem in a steelmaking process. Shortly before the tapping of molten steel from the basic oxygen furnace (BOF), end-point control measures are applied to achieve the required final molten steel quality. While it is difficult to build an exact mathematical model for this process, the control inputs and the corresponding outputs are available by collecting production data. We build a data-driven model for the process. To optimize the control parameters, an improved estimation of distribution algorithm (EDA) is developed using a probabilistic model comprising different distributions. A resampling mechanism is incorporated into the EDA to guide the new population to a broader and more promising area when the search becomes ineffective. To further enhance the solution quality, we add a local improvement to update the current best individual through simplified gravitational search and information learning. Experiments are conducted using real data from a BOF steelmaking process. The results show that the algorithm can help to achieve the specified molten steel quality. To evaluate the proposed algorithm as a general optimization algorithm, we test it on some complex benchmark functions. The results illustrate that it outperforms other state-of-the-art algorithms across a wide range of problems.
科研通智能强力驱动
Strongly Powered by AbleSci AI