Design Variables Optimization of Mechanical Problems

Author

Lee, Kuo-Ming ; Tsai, Jinn-Tsong ; Ho, Wen-Hsien ; Liu, Tung-Kuan ; Chou, Jyh-Horng

Author_Institution

Inst. of Eng. Sci. & Technol., Nat. Kaohsiung First Univ. of Sci. & Technol., Kaohsiung

fYear

2008

fDate

18-20 June 2008

Firstpage

315

Lastpage

315

Abstract

An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select the better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions.

Keywords

genetic algorithms; mechanical engineering computing; nonlinear programming; IGA; MDCNLP; crossover operations; design variables optimization; experimental design method; improved genetic algorithm; mechanical problems; mixed-discrete-continuous nonlinear programming; representative chromosomes; Algorithm design and analysis; Biological cells; Computer industry; Design engineering; Design for experiments; Design optimization; Educational technology; Genetic algorithms; Genetic engineering; Research and development;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on

Conference_Location

Dalian, Liaoning

Print_ISBN

978-0-7695-3161-8

Electronic_ISBN

978-0-7695-3161-8

Type

conf

DOI

10.1109/ICICIC.2008.642

Filename

4603504