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dc.contributor.author여영구-
dc.date.accessioned2019-11-24T17:45:31Z-
dc.date.available2019-11-24T17:45:31Z-
dc.date.issued2017-04-
dc.identifier.citationJOURNAL OF CHEMICAL ENGINEERING OF JAPAN, v. 50, no. 4, page. 273-290en_US
dc.identifier.issn0021-9592-
dc.identifier.urihttps://www.jstage.jst.go.jp/article/jcej/50/4/50_16we002/_article-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/113782-
dc.description.abstractThis paper presents a novel Teaching-Learning-Self-Study-Optimization (TLSO) algorithm which is not only fast converging according to the number of iterations, but also relatively consistent in converging with high accuracy to the global minimum in comparison with some other algorithms. The original Teaching-Learning-Based Optimization (TLBO) gives uniformly distributed and randomly selected weight to the amount of knowledge to a learner at each phase, i. e., teacher phase and learner phase. This uniformly distributed and randomly selected weight causes the algorithm to converge the average cost of learners in a moderate number of iterations. Li and his coworkers intensified the teacher and learner phases by introducing weight-parameters in order to improve the convergence speed in terms of iterations in 2013 and called it Ameliorated Teaching-Learning-Based Optimization (ATLBO). The criterion of a good evolutionary optimization algorithm is to be consistent in converging the cost of the objective function. For this, it should include intensification for local search as well as diversification for global search in order to reduce the chances of trapping in a local minimum. Some students naturally tend to study by themselves by the means of a library and internet academic resources in order to enhance their knowledge. This phenomenon is termed as self-study and is introduced in the proposed TLSO's learner phase as a diversification factor (DF). Various other evolutionary algorithms such as ACO, PSO, TLBO, ATLBO and two variants of TLSO are also developed and compared with TLSO in terms of consistency to converge to the global minimum. Results reveal that the TLSO was found to be consistent not only for a higher number of functions among 20 benchmark functions, but also for NOx prediction application. Results also show that the predicted NOx emissions through LSSVM tuned with TLSO are comparable with the other algorithms considered in this work.en_US
dc.description.sponsorshipThis research work is sponsored by the Higher Education Commission (HEC), Govt. of Pakistan under the scholarship program titled: "HRDI-MS leading to PhD program of faculty development for UESTPs/UETs (Batch-II) Phase-I". We also would like to thank the staff of the Taean Power Plant, Taean, South Korea for their technical Support.en_US
dc.language.isoen_USen_US
dc.publisherSOC CHEMICAL ENG JAPANen_US
dc.subjectLeast Squares Support Vector Machinesen_US
dc.subjectNOx Predictionen_US
dc.subjectTeaching-Learning-Based-Optimizationen_US
dc.subjectAmeliorated Teaching-Learning-Based-Optimizationen_US
dc.subjectTeaching-Learning-Self-Study-Optimizationen_US
dc.titleA Fast Converging and Consistent Teaching-Learning-Self-Study Algorithm for Optimization: A Case Study of Tuning of LSSVM Parameters for the Prediction of NOx Emissions from a Tangentially Fired Pulverized Coal Boileren_US
dc.typeArticleen_US
dc.relation.no4-
dc.relation.volume50-
dc.identifier.doi10.1252/jcej.16we002-
dc.relation.page273-290-
dc.relation.journalJOURNAL OF CHEMICAL ENGINEERING OF JAPAN-
dc.contributor.googleauthorAhmed, Faisal-
dc.contributor.googleauthorKim, Jin-Kuk-
dc.contributor.googleauthorKhan, Asad Ullah-
dc.contributor.googleauthorPark, Ho Young-
dc.contributor.googleauthorYeo, Yeong Koo-
dc.relation.code2017000316-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF CHEMICAL ENGINEERING-
dc.identifier.pidykyeo-
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COLLEGE OF ENGINEERING[S](공과대학) > CHEMICAL ENGINEERING(화학공학과) > Articles
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