The use of surrogate modeling techniques to efficiently solve a single objective optimization (SOO) problem has proven its worth in the optimization community. However, industrial problems are often characterized by multiple conflicting and constrained objectives. Recently, a number of infill criteria have been formulated to solve multi-objective optimization (MOO) problems using surrogates and to determine the Pareto front. Nonetheless, to accurately resolve the front, a multitude of optimal points must be determined, making MOO problems by nature far more expensive than their SOO counterparts. As of yet, even though access to of high performance computing is widely available, little importance has been attributed to batch optimization and asynchronous infill methodologies, which can further decrease the wall-clock time required to determine the Pareto front with a given resolution. In this paper a novel infill criterion is developed for generalized asynchronous multi-objective constrained optimization, which allows multiple points to be selected for evaluation in an asynchronous manner while the balance between design space exploration and objective exploitation is adapted during the optimization process in a simulated annealing like manner and the constraints are taken into account. The method relies on a formulation of the expected improvement for multi-objective optimization, where the improvement is formulated as the Euclidean distance from the Pareto front taken to a higher power. The infill criterion is tested on a series of test cases and proves the effectiveness of the novel scheme.

使用代理建模技术来有效解决单个目标优化（SOO）问题已在优化社区中证明了其价值。但是，工业问题通常以目标相互矛盾和受限为特征。最近，已经制定了许多填充标准，以使用替代解决多目标优化（MOO）问题并确定帕累托前沿。但是，要准确解决问题，必须确定多个最佳点，这使MOO问题天生比SOO同行要昂贵得多。到目前为止，即使可以广泛使用高性能计算，但批处理优化和异步填充方法论的重要性仍然很少，这样可以进一步减少在给定分辨率下确定帕累托前锋所需的挂钟时间。本文针对通用异步多目标约束优化设计了一种新的填充准则，该准则允许选择多个点以异步方式进行评估，同时在仿真过程中优化过程中适应设计空间探索和目标开发之间的平衡像退火一样的方式和约束被考虑在内。该方法依赖于对多目标优化的预期改进公式，该改进公式表示为从帕累托前沿到更高幂的欧几里德距离。在一系列测试案例中测试了填充标准，并证明了该新方案的有效性。