Evolutionary Algorithms for Optimization of Tobacco Leaf Groups Blending

Author

Peiyong, Xia ; Xiangqian, Ding ; Ning, Yang

Author_Institution

Coll. of Inf. Sci. & Eng., Ocean Univ. of China, Qingdao, China

fYear

2009

fDate

27-29 May 2009

Firstpage

144

Lastpage

148

Abstract

Traditional methods using for the design of tobacco leaf groups blending depend mostly on expert experiences. But they are lack control of the product quality and proved inefficient in practice. In this paper, we use the modified GA and PSO algorithms to help to optimize the leaf groups. The experimental results demonstrated that the modified GA and PSO algorithms are faster and more accurate when compared with the traditional methods; meanwhile PSO performs better than GA in General conditions.

Keywords

CAD/CAM; blending; genetic algorithms; manufactured products; particle swarm optimisation; quality control; tobacco industry; tobacco products; computer aided blending design; evolutionary algorithm; genetic algorithm; material blending design; particle swarm optimisation; product manufacturer; product quality control; tobacco leaf group blending optimization; Artificial intelligence; Biological cells; Biology computing; Design optimization; Distributed computing; Evolution (biology); Evolutionary computation; Genetic mutations; Intelligent networks; Software engineering; Evolutionary Optimization; GA; PSO; Tobacco Leaf Groups Blending;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, 2009. SNPD '09. 10th ACIS International Conference on

Conference_Location

Daegu

Print_ISBN

978-0-7695-3642-2

Type

conf

DOI

10.1109/SNPD.2009.91

Filename

5286679