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

摘要：

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 MA TLAB. 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.

关键词： ant algorithm mixed-discrete variables support vector machine robust design optimization

作者: Ren Pishun Hart Huixian Guo Huixin

作者单位: Department of Mechanical Engineering Hunan Mechanical & Electrical polytechnic Changsha, China Department of Mechanical and Electrical Engineering Changsha University,Changsha, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 472-475

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）