存在非常嘈杂的评估结果时的全局优化的信息方法。应用于可再生能源整合策略的优化
The Informational Approach to Global Optimization in presence of very noisy evaluation results. Application to the optimization of renewable energy integration strategies
摘要：
我们考虑从非常嘈杂的评价函数f的全局优化的问题。我们采用贝叶斯序贯方法：选择评估点以减少关于f的全局最优位置的不确定性，如通过相应随机变量的熵所测量的（全局优化的信息方法，Villemonteix等人，2009年）。当评估是非常嘈杂的，来自使用条件模拟的熵的估计的误差变得不可忽略相比，其在输入域上的变化。我们提出了一个解决这个问题的方法，选择评价点，如果几个评价将在这些点。该方法被施加到可再生能源的整合到配电网的策略的优化。
英文摘要：
We consider the problem of global optimization of a function f from very noisy evaluations. We adopt a Bayesian sequential approach: evaluation points are chosen so as to reduce the uncertainty about the position of the global optimum of f, as measured by the entropy of the corresponding random variable (Informational Approach to Global Optimization, Villemonteix et al., 2009). When evaluations are very noisy, the error coming from the estimation of the entropy using conditional simulations becomes non negligible compared to its variations on the input domain. We propose a solution to this problem by choosing evaluation points as if several evaluations were going to be made at these points. The method is applied to the optimization of a strategy for the integration of renewable energies into an electrical distribution network.
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