Since uncertainties in variables are unavoidable, an optimal solution must consider the robustness of the design. The gradient index approach provides a convenient way to evaluate the robustness but is inconclusive when several possible solutions exist. To overcome this limitation, a novel methodology based on the use of first- and second-order gradient indices is proposed introducing the notion of gradient sensitivity. The sensitivity affords a measure of the change in the objective function with respect to the uncertainty of the variables. A Kriging method assisted by algorithms exploiting the concept of rewards is employed to facilitate function predictions for the robust optimisation process. The performance of the proposed algorithm is assessed through a series of numerical experiments. A modification to the correlation model through the introduction of a Kriging predictor and mean square error criterion allows efficient solution of large scale and multi-parameter problems. The three parameter version of TEAM Workshop Problem 22 has been used for illustration.
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机译：由于变量的不确定性不可避免，因此最佳解决方案必须考虑设计的稳健性。梯度指数方法提供了一种评估鲁棒性的便捷方法，但是在存在几种可能的解决方案时并不确定。为了克服此限制，提出了一种基于使用一阶和二阶梯度指数的新颖方法，引入了梯度灵敏度的概念。灵敏度提供了关于变量不确定性的目标函数变化的量度。采用克里格方法，该算法由利用奖励概念的算法辅助，以促进功能强大的优化过程的功能预测。通过一系列数值实验评估了该算法的性能。通过引入Kriging预测因子和均方误差准则对相关模型进行修改，可以有效解决大规模和多参数问题。 TEAM Workshop Problem 22的三个参数版本已用于说明。
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