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A note on the use of a fuzzy approach in adaptive partitioning algorithms for global optimization

机译:关于在全局优化的自适应分区算法中使用模糊方法的说明

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摘要

In global optimization, adaptive partitioning algorithms (APA) operate on the basis of partitioning the feasible region into subregions, sampling and evaluating each subregion, and selecting one or more subregions for repartitioning. The purpose of the repartitioning process is to locate a narrow neighborhood around the global optimum. In this correspondence, we propose to use a fuzzy approach in the assessment of subregions using random samples taken from these subregions. We discuss different types of uncertainties involved in APA and we conclude that the use of a fuzzy approach in the assessment of subregions is in concurrence with APA's convergence property. We provide numerical results for the fuzzy approach on 13 test functions from the literature.
机译:在全局优化中,自适应分区算法(APA)在将可行区域划分为子区域,对每个子区域进行采样和评估以及选择一个或多个子区域进行重新划分的基础上进行操作。重新分区过程的目的是在全局最优值附近找到一个狭窄的邻域。在这种对应关系中,我们建议使用模糊方法从来自这些子区域的随机样本中评估这些子区域。我们讨论了APA中涉及的不同类型的不确定性,并得出结论,在评估次区域中使用模糊方法与APA的收敛性是一致的。我们从文献中为13种测试函数的模糊方法提供了数值结果。

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