Dynamic Multi-objective Operation Optimization of Blast Furnace Based on Evolutionary Algorithm
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2272-3_19
Reference13 articles.
1. Agrawal, A., Agarwal, M.K., Kothari, A.K., Mallick, S.: A mathematical model to control thermal stability of blast furnace using proactive thermal indicator. Ironmaking & Steelmaking 46(2), 133–140 (2019). https://doi.org/10.1080/03019233.2017.1353765
2. Cheng, R., Jin, Y., Olhofer, M., Sendhoff, B.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016). https://doi.org/10.1109/TEVC.2016.2519378
3. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011). https://doi.org/10.1016/j.swevo.2011.02.002
4. Li, Y., Li, H., Zhang, J., Zhang, S., Yin, Y.: Burden surface decision using mode with topsis in blast furnace ironmkaing. IEEE Access 8, 35712–35725 (2020). https://doi.org/10.1109/ACCESS.2020.2974882
5. Mahanta, B.K., Chakraborti, N.: Tri-objective optimization of noisy dataset in blast furnace iron-making process using evolutionary algorithms. Mater. Manuf. Processes 35(6), 677–686 (2020). https://doi.org/10.1080/10426914.2019.1643472
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