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dc.contributor.authorWang, Kangen
dc.contributor.authorLi, Xiaolien
dc.contributor.authorJia, Chaoen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorLi, Miqingen
dc.contributor.authorLi, Yangen
dc.date.accessioned2017-08-11T10:05:27Z
dc.date.available2017-08-11T10:05:27Z
dc.date.issued2017-08-04
dc.identifier.citationWang, K. et al. (2017) Multiobjective optimization of the production process for ground granulated blast furnace slags. Soft Computing, 22 (24), pp. 8177-8186en
dc.identifier.urihttp://hdl.handle.net/2086/14415
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractThe production process of ground granulated blast furnace slag (GGBS) aims to produce products of the best grade and the highest yields. However, grade and yields are two competing objectives which can not be optimized at the same time by one single solution. Meanwhile, the production process is a multivariable strong coupling complicated nonlinear system. It is hard to establish the accurate mechanism model of this system. Considering above problems, we formulate the GGBS production process as an multiobjective optimization problem, introduce a least square support vector machine method based on particle swarm optimization to build the data-based system model and solve the corresponding multiobjective optimization problem by several multiobjective optimization evolutionary algorithms. Simulation example is presented to illustrate the performance of the presented multiobjective optimization scheme in GGBS production process.en
dc.language.isoen_USen
dc.publisherSpringeren
dc.subjectGround granulated blast furnace slagen
dc.subjectMultiobjective optimizationen
dc.subjectMOEAen
dc.subjectPSO-based LS-SVMen
dc.titleMultiobjective optimization of the production process for ground granulated blast furnace slagsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1007/s00500-017-2761-x
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.funderNational Natural Science Foundation of Chinaen
dc.projectid61473034en
dc.projectid61673053en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-07-26en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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