Scalarizing Functions in Bayesian Multiobjective Optimization
Scalarizing Functions in Bayesian Multiobjective Optimization
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DOI：
10.1109/cec48606.2020.9185706
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发表时间：
2019-04
期刊：
影响因子：
--
通讯作者：
Tinkle Chugh
中科院分区：
文献类型：
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作者：
Tinkle Chugh
标量函数被广泛用于将多目标优化问题转化为单目标优化问题。然而，它们在利用贝叶斯多目标优化解决计算量大的多目标和多目标优化问题中的应用很少。在进行优化时，缩放函数对所需评估的质量和数量起着至关重要的作用。本文比较了贝叶斯多目标优化框架下的15种不同的标度函数，并在其上建立了高斯过程模型。我们使用预期的改进作为填充标准（或获取函数）来更新模型。特别地，我们分析了不同尺度函数在不同目标数的基准问题上的性能。对不同函数的回顾和实验为使用标量函数提供了有用的见解，特别是对于具有大量目标的问题。
Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving computationally expensive multi-and many-objective optimization problems using Bayesian multiobjective optimization is scarce. Scalarizing functions can play a crucial role on the quality and number of evaluations required when doing the optimization. In this article, we compare 15 different scalarizing functions in the framework of Bayesian multiobjective optimization and build Gaussian process models on them. We use the expected improvement as infill criterion (or acquisition function) to update the models. In particular, we analyze the performance of different scalarizing functions on several benchmark problems with different number of objectives to be optimized. The review and experiments on different functions provide useful insights in using a scalarizing function, especially for problems with a large number of objectives.