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DC Field | Value | Language |
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dc.rights.license | openAccess | - |
dc.contributor.author | Ristic, Olga | - |
dc.contributor.author | Milunović Koprivica, Sandra | - |
dc.contributor.author | Milošević, Marjan | - |
dc.date.accessioned | 2022-09-13T10:10:12Z | - |
dc.date.available | 2022-09-13T10:10:12Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9788677762629 | en_US |
dc.identifier.uri | https://scidar.kg.ac.rs/handle/123456789/14848 | - |
dc.description | This paper is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, and these results are part of the Grant No. 451-03-68/2022-14/200132 with the University of Kragujevac-Faculty of Technical Sciences Čačak. | en_US |
dc.description.abstract | Social Human Behaviour algorithms are the next step in nature inspired algorithms development. In the past decade these are proved to be useful for various optimisation tasks. The paper provided a global preview of existing algorithms of this kind and focused on two specific algorithms, inspired by teaching and learning process: Teaching Learning Based Optimization and Group Teaching Optimisation algorithms. The algorithms' structure and flow are thoroughly explained and illustrated. A preview of algorithms' application is reported, based on the recent research. It is concluded that this kind of algorithms can be aplied in various industry areas and that further research in this field is reqired. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Kragujevac, Faculty od Technical Sciences, Čačak | en_US |
dc.rights | info:eu-repo/semantics/openAccess | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.source | “Technics and Informatics in Education – TIE 2022” | en_US |
dc.source | Proceedings TIE 2022 9th International Scientific Conference Technics and Informatics in Education | en_US |
dc.subject | TLBO | en_US |
dc.subject | GTO | en_US |
dc.subject | algorithm | en_US |
dc.subject | teaching | en_US |
dc.subject | learning | en_US |
dc.title | Teaching and learning inspired optimization algorithms: A review | en_US |
dc.type | conferenceObject | en_US |
dc.description.version | Published | en_US |
dc.identifier.doi | 10.46793/TIE22.302R | en_US |
dc.type.version | PublishedVersion | en_US |
Appears in Collections: | Faculty of Technical Sciences, Čačak |
Files in This Item:
File | Description | Size | Format | |
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S413_25.pdf | 845.12 kB | Adobe PDF | View/Open |
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