Bare-bones teaching-learning-based optimization.
Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to s...
| Published in: | Scientific World Journal pp. 136920 - 136921 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Wiley-Blackwell
2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103833371&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103833371 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103833371 103833371 NLM25013844 2012644068 10.1155/2014/136920 NLM25013844 PMC4071861 103833371 ppf: 136920 ppct: 1 formats: tig: atl: Bare-bones teaching-learning-based optimization. aug: au: Zou, Feng Wang, Lei Hei, Xinhong Chen, Debao Jiang, Qiaoyong Li, Hongye affil: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China ; School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China. sug: subj: Algorithms Learning Teaching ab: Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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