Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
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发表时间：
2020
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通讯作者：
Mina Konakovic-Lukovic;Yunsheng Tian;W. Matusik
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作者：
Mina Konakovic-Lukovic;Yunsheng Tian;W. Matusik
许多科学、工程和设计优化问题需要在几个相互冲突的目标之间进行权衡。目标通常是黑盒函数，其评估既耗时又昂贵。多目标贝叶斯优化可用于自动化发现最优解集合的过程，称为帕累托最优，同时最小化执行的评估次数。为了进一步减少优化过程中的评估时间，可以并行部署多个样本的测试。我们提出了一种新的多目标贝叶斯优化算法，该算法迭代地选择最优的一批样本进行并行评估。我们的算法对分段连续的Pareto集表示进行了逼近和分析。这种表示允许我们引入一种批次选择策略，该策略针对所选样本的超大容量改进和多样性进行优化，以便有效地推进帕累托前沿的有希望的区域。在合成测试函数和真实基准问题上的实验表明，该算法的性能明显优于相关的最新方法。代码可在https://github.com/yunshengtian/DGEMO.上获得
Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often blackbox functions whose evaluations are time-consuming and costly. Multi-objective Bayesian optimization can be used to automate the process of discovering the set of optimal solutions, called Pareto-optimal, while minimizing the number of performed evaluations. To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed. We propose a novel multi-objective Bayesian optimization algorithm that iteratively selects the best batch of samples to be evaluated in parallel. Our algorithm approximates and analyzes a piecewise-continuous Pareto set representation. This representation allows us to introduce a batch selection strategy that optimizes for both hypervolume improvement and diversity of selected samples in order to efficiently advance promising regions of the Pareto front. Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods. The code is available at https://github.com/yunshengtian/DGEMO.