Robust Design Optimization with Mixed-Discrete Variables Based on Ant Algorithm and Support Vector Machine

Author

Pishun, Ren ; Huixian, Han ; Huixin, Guo

Author_Institution

Dept. of Mech. Eng., Hunan Mech. & Electr. Polytech., Changsha, China

Volume

1

fYear

2009

fDate

10-11 Oct. 2009

Firstpage

472

Lastpage

475

Abstract

The basic ant optimization algorithm is improved by introducing ant colony scatterance and discrete search. In order to solve the optimization problem with mixed-discrete variables, a program of ant algorithm is designed by using MATLAB. Based on the introduce of support vector regression (SVR) which is used to compute the values of nonlinear functions such as fuzzy probability, the computational efficiency of robust design optimization is distinctly improved. An example of robust design optimization with mixed-discrete variables is presented, and it shows that the proposed method is effective in engineering application.

Keywords

optimisation; search problems; support vector machines; MATLAB; ant colony scatterance; ant optimization algorithm; computational efficiency; design optimization; discrete search; fuzzy probability; mixed-discrete variables; nonlinear functions; support vector machine; support vector regression; Ant colony optimization; Design automation; Design engineering; Design methodology; Design optimization; Heuristic algorithms; MATLAB; Machine intelligence; Noise robustness; Support vector machines; ant algorithm; mixed-discrete variables; robust design optimization; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Conference_Location

Changsha, Hunan

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.121

Filename

5287611