Multi-objective robust optimization with random and interval variables
Many engineering optimization problems are multi-objective and have uncertainty.It is desirable to obtain solutions that are multi-objectively optimum and robust.In this work, the uncertain parameters with sufficient information are treated by random distributions, while some ones with limited information can only be given variation intervals.The robust multi-objective optimization model thus can be formulated with mixed variables.The mean and variance of random interval variables are calculated by the Monte Carlo simulation in the inner layer.Then, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are adopted as optimization operator in the outer layer.To improve the optimization efficiency, the response surface approximation models are constructed for the uncertain objective and constraint functions based on the Latin Hypercube Design(LHD).The multi-objective robust optimization method is combined with the approximation models to form an efficient robust multi-objective optimization method.The numerical examples are presented to demonstrate the effectiveness of the proposed method.
multi-objective optimization robust random interval hybrid uncertainty
Fangyi Li Jianhua Rong
School of Automotive and mechanical Engineering,Changsha University of Science and Technology,Changsha,410114,China
国际会议
黄山
英文
1-19
2012-06-18（万方平台首次上网日期，不代表论文的发表时间）