论文标题
减轻贝叶斯进化优化的搜索偏见，并具有许多异质目标
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
论文作者
论文摘要
在现实世界中通常会看到其目标具有不同评估成本的多目标优化问题。现在，此类问题被称为异质目标（HE-MOPS）的多目标优化问题。然而，到目前为止，只有少数研究来解决HE-MOPS，其中大多数专注于一个快速目标和一个缓慢目标的双目标问题。在这项工作中，我们旨在应对具有两个以上黑盒和异质目标的He-mops。为此，我们通过利用He-Mops中廉价且昂贵的目标来利用不同的数据集来减轻因评估不同目标而导致的搜索偏见，从而减轻搜索偏见，以减轻评估不同目标的搜索偏见，从而为HE-MOPS开发了多目标贝叶斯进化优化方法。为了充分利用两个不同的培训数据集，一种对所有目标进行评估的解决方案，另一个对仅在快速目标上进行评估的解决方案进行了评估，构建了两个单独的高斯过程模型。此外，提出了一种新的采集函数，以减轻对快速目标的搜索偏见，从而在收敛与多样性之间达到平衡。我们通过对广泛使用的多/多目标基准问题进行测试来证明该算法的有效性，这些问题被认为是异质昂贵的。
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimization problems with heterogeneous objectives (HE-MOPs). So far, however, only a few studies have been reported to address HE-MOPs, and most of them focus on bi-objective problems with one fast objective and one slow objective. In this work, we aim to deal with HE-MOPs having more than two black-box and heterogeneous objectives. To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives. To make the best use of two different training data sets, one with solutions evaluated on all objectives and the other with those only evaluated on the fast objectives, two separate Gaussian process models are constructed. In addition, a new acquisition function that mitigates search bias towards the fast objectives is suggested, thereby achieving a balance between convergence and diversity. We demonstrate the effectiveness of the proposed algorithm by testing it on widely used multi-/many-objective benchmark problems whose objectives are assumed to be heterogeneously expensive.
