The multi-objective efficient global optimization (MOEGO), an extension of the single-objective efficient global optimization algorithm with the intention to handle multiple objectives, is one of the most frequently studied surrogate model-based optimization algorithms. However, the evaluation of the infill point obtained in each MOEGO update iteration using simulation tool may fail. Such evaluation failures are critical to the sequential MOEGO method as it leads to a premature halt of the optimization process due to the impossibility of updating the Kriging models approximating objectives. In this paper, a novel strategy to prevent the premature halt of the sequential MOEGO method is proposed. The key point is to introduce an additional Kriging model to predict the success possibility of the simulation at an unvisited point. Multi-objective expected improvement-based criteria incorporating the success possibility of the simulation are proposed. Experiments are performed on a set of six analytic problems, five low-fidelity airfoil shape optimization problems, and a high-fidelity axial flow compressor tandem cascade optimization problem. Results suggest that the proposed MOEGO-Kriging method is the only method that consistently performs well on analytic and practical problems. The methods using the least-square support vector machine (LSSVM) or weighted LSSVM as the predictor of success possibility perform competitively or worse compared with MOEGO-Kriging. The penalty-based method, assigning high objective values to the failed evaluations in minimization problem, yields the worst performance.

多目标高效全局优化（MOEGO）是单目标高效全局优化算法的扩展，旨在处理多个目标，是最常研究的基于替代模型的优化算法之一。但是，使用仿真工具在每个MOEGO更新迭代中获得的填充点的评估可能会失败。此类评估失败对于顺序式MOEGO方法至关重要，因为由于无法更新近似目标的Kriging模型而导致优化过程过早停止。本文提出了一种防止顺序MOEGO方法过早停止的新策略。关键是引入一个额外的Kriging模型，以预测在未访问的地点进行模拟的成功可能性。提出了基于多目标期望改进的准则，并结合了模拟的成功可能性。在一组六个分析问题，五个低保真翼型形状优化问题和一个高保真轴流压缩机串联级联优化问题上进行了实验。结果表明，提出的MOEGO-Kriging方法是唯一在分析和实际问题上始终表现良好的方法。与MOEGO-Kriging相比，使用最小二乘支持向量机（LSSVM）或加权LSSVM作为成功可能性的预测指标的方法具有竞争优势，甚至更差。基于罚分的方法将极高的目标值分配给最小化问题中的失败评估，导致性能最差。