Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

Cited 9 time in webofscience Cited 0 time in scopus
  • Hit : 420
  • Download : 0
This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2015-04
Language
English
Article Type
Article
Keywords

PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION; DESIGN OPTIMIZATION; GLOBAL OPTIMIZATION; IMMUNE ALGORITHM; HYBRIDIZATION; INDUSTRY; SHAPE

Citation

ENGINEERING OPTIMIZATION, v.47, no.4, pp.521 - 532

ISSN
0305-215X
DOI
10.1080/0305215X.2014.905551
URI
http://hdl.handle.net/10203/195350
Appears in Collection
ME-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 9 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0