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An estimation of distribution algorithm with resampling and local improvement for an operation optimization problem in steelmaking process

journal contribution
posted on 07.01.2020 by Lixin Tang, Chang Liu, Jiyin Liu, Xianpeng Wang
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


  • Business

Published in

IEEE Transactions on Systems Man and Cybernetics: Systems


Institute of Electrical and Electronics Engineers


AM (Accepted Manuscript)

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date









Prof Jiyin Liu. Deposit date: 4 January 2020


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