引用本文:杨爱民,李杰,刘卫星,张良进,闫龙格.考虑灵敏度区域的多目标鲁棒性优化算法[J].控制理论与应用,2016,33(2):205~211.[点击复制]
YANG Ai-min,LI Jie,LIU Wei-xing,ZHANG Liang-jin,YAN Long-ge.The multi-objective robust optimization method considering sensitivity region[J].Control Theory and Technology,2016,33(2):205~211.[点击复制]
考虑灵敏度区域的多目标鲁棒性优化算法
The multi-objective robust optimization method considering sensitivity region
摘要点击 3874  全文点击 2679  投稿时间：2014-12-14  修订日期：2015-08-12
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DOI编号  10.7641/CTA.2016.41159
  2016,33(2):205-211
中文关键词  灵敏度  多目标鲁棒性  鲁棒性指数  区间变量  最坏情况
英文关键词  sensitivity region  multi-objective robust  robustness index  interval variables  the worst-case
基金项目  国家自然科学基金项目(51504080)资助.
作者单位E-mail
杨爱民* 华北理工大学 Aiminyang@126.com 
李杰 华北理工大学  
刘卫星 华北理工大学  
张良进 华北理工大学  
闫龙格 华北理工大学  
中文摘要
      随机变量导致工程问题具有不确定性. 设计者希望设计方案不仅能满足目标性能最优, 而且希望目标性能 受不确定性的影响在可接受范围之内. 对此, 本文提出了考虑灵敏度区域的多目标鲁棒性优化方法(multi-objective robust optimization based on sensitivity region, SR–MORO). SR–MORO可以用来解决设计变量存在不确定性时目标 鲁棒性优化设计问题. 该方法假定不确定性变量属于区间变量, 并不需要知道随机变量的概率分布. SR–MORO采 用非梯度优化方法, 所以, 它可以解决目标函数和约束条件不连续的情况. 当参数变化幅度大, 超过目标函数线性 变化范围, 该方法也同样适用. 最后, 通过实例验证了本方法的适用性
英文摘要
      Engineering design problems are usually uncertain because of the random variables. Designers want to design scheme to meet the goal of not only the best performance, but also want to target performance is affected by the uncertainty within the acceptable range. To solve this problem, we propose a multi-objective robust optimization method considering sensitivity region (multi-objective robust optimization based on sensitive region, SR–MORO). SR–MORO can be used to solve optimization design problem involving uncertainty design variables. This method assumes that the uncertainty variables belong to the interval variables, so it does not need to know the probability distribution of random variables. Nongradient optimization method is used to solve the robust optimization problem, so that the approach is applicable for cases that have discontinuous objective and constraint functions with respect to uncontrollable parameters. When the parameters changed much over the linear range of the objective function, the method is also applicable. Finally, the applicability is also verified by an example.