论文标题
具有基于Bézier单纯形的插值方法的两阶段框架，用于计算昂贵的多目标优化
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
论文作者
论文摘要
本文提出了一个具有基于Bézier简称的插值方法（TPB）的两阶段框架，以进行计算昂贵的多目标优化。 TPB中的第一阶段旨在通过优化一系列单目标标量问题来近似一些帕累托最佳解决方案。 TPB中的第一阶段可以充分利用最先进的单目标无衍生物优化器。 TPB中的第二阶段利用Bézier单纯形模型插值在第一阶段获得的解决方案。 TPB中的第二阶段充分利用了这样一个事实，即Bézier单纯形模型可以通过利用其单纯形结构在给定的问题很简单时近似于帕累托最佳解决方案。我们研究了TPB在55个双向目标BBOB问题上的性能。结果表明，TPB的性能明显优于HMO-CMA-E和一些基于元模型的优化器。
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems. The first phase in TPB can fully exploit a state-of-the-art single-objective derivative-free optimizer. The second phase in TPB utilizes a Bézier simplex model to interpolate the solutions obtained in the first phase. The second phase in TPB fully exploits the fact that a Bézier simplex model can approximate the Pareto optimal solution set by exploiting its simplex structure when a given problem is simplicial. We investigate the performance of TPB on the 55 bi-objective BBOB problems. The results show that TPB performs significantly better than HMO-CMA-ES and some state-of-the-art meta-model-based optimizers.
