In this paper, a novel Kriging-based algorithm for multiobjective optimization of expensive-to-evaluate black-box functions is proposed. The algorithm is based on sequential reduction of the entropy of the predicted Pareto front. The associated infill criterion selects a candidate design with the highest informational entropy among a set of predicted Pareto designs. The algorithm is tested on three engineering benchmark problems: the Nowacki cantilever beam, a car-side impact problem and a water management problem. The algorithm is also used to find the Pareto front for a snap-fit design. In the benchmark problems, the proposed algorithm outperformed traditional ones (parEGO and EHVI) when three different performance indicators were considered. The results also suggest that the algorithm is robust and produces a wide, representative Pareto front.

在本文中，提出了一种新的基于Kriging的算法，用于多目标优化评估黑盒函数。该算法基于预测Pareto前沿的熵的顺序减小。关联的填充标准在一组预测的Pareto设计中选择具有最高信息熵的候选设计。该算法在三个工程基准问题上进行了测试：NoWacki悬臂梁，汽车侧面碰撞问题和水管理问题。该算法还用于找到扣合设计的Pareto前沿。在基准问题中，当考虑三个不同的性能指标时，提出的算法优于传统算法（parEGO和EHVI）。结果还表明该算法是鲁棒的，并产生宽泛的代表性Pareto前沿。