File(s) under embargo
Reason: Awaiting publication
until file(s) become available
An estimation of distribution algorithm with resampling and local improvement for an operation optimization problem in steelmaking process
journal contributionposted on 07.01.2020 by Lixin Tang, Chang Liu, Jiyin Liu, Xianpeng Wang
Any type of content formally published in an academic journal, usually following a peer-review process.
This paper studies an operation optimization problem in a steelmaking process. Shortly before the tapping of molten steel from the converter furnace, 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 converter 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.
Major International Joint Research Project of the National Natural Science Foundation of China under Grant 71520107004
Major Program of National Natural Science Foundation of China under Grant 71790614
Fund for Innovative Research Groups of the National Natural Science Foundation of China under Grant 71621061
111 Project under Grant B16009
National Natural Science Foundation of China under Grant 61702077
National Natural Science Foundation of China under Grant 61573086
- Business and Economics