An Improved Teaching-Learning Based Optimization for Optimization of Flatness of a Strip During a Coiling Process

被引：1

作者：

Bureerat, Sujin ^{[1
]}

Pholdee, Nantiwat ^{[1
]}

Park, Won-Woong ^{[2
]}

Kim, Dong-Kyu ^{[3
]}

机构：

[1] Khon Kaen Univ, Fac Engn, Sustainable & Infrastruct Res & Dev Ctr, Dept Mech Engn, 123 Moo 16 Mittraphap Rd, Khon Kaen, Thailand

[2] Korea Adv Inst Sci & Technol, Natl Res Lab Comp Aided Mat Proc, Dept Mech Engn, Daejeon, South Korea

[3] Korea Atom Energy Res Inst, Div Neutron Sci, Daejeon, South Korea

来源：

MULTI-DISCIPLINARY TRENDS IN ARTIFICIAL INTELLIGENCE, (MIWAI 2016) | 2016年 / 10053卷

关键词：

Evolutionary algorithm; Flatness defect; Optimization; Strip coiling; Teaching-learning based optimization; HYBRID EVOLUTIONARY ALGORITHM; MULTIOBJECTIVE OPTIMIZATION; DIFFERENTIAL EVOLUTION; DESIGN; HYBRIDIZATION; PREDICTION; PRODUCT; SHAPE;

D O I：

10.1007/978-3-319-49397-8_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Performance enhancement of a teaching-learning basedz optimizer (TLBO) for strip flatness optimization during a coiling process is proposed. The method is termed improved teaching-learning based optimization (ITLBO). The new algorithm is achieved by modifying the teaching phase of the original TLBO. The design problem is set to find spool geometry and coiling tension in order to minimize flatness defects during the coiling process. Having implemented the new optimizer with flatness optimization for strip coiling, the results reveal that the proposed method gives a better optimum solution compared to the present state-of-the-art methods.

引用

页码：12 / 23

页数：12

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