Design optimization problems with black-box computation-intensive objective and constraints are extremely challenging in engineering practices. To address this issue, an efficient metamodel-based optimization strategy using parallel adaptive kriging method with constraint aggregation (PAKM-CA) is proposed. In PAKM-CA, the complex expensive constraints are aggregated using the Kreisselmeier and Steinhauser (KS) function. Besides, based on the notion of Pareto nondomination in terms of objective optimality and KS function feasibility, a novel parallel comprehensive feasible expected improvement (PCFEI) function considering the correlations of sample points is developed to effectively determine the sequential infill sample points. The infill sample points with the highest PCFEI function values are selected to dynamically refine the kriging metamodels, which simultaneously improves the optimality and feasibility of optimization. Moreover, the optimization time can be further reduced via the parallel sampling framework of PCFEI. Then the convergence and efficiency merits of PAKM-CA are demonstrated via comparing with competitive state-of-the-art metamodel-based constrained optimization methods on numerical benchmarks. Finally, PAKM-CA is applied to a practical long-range slender guided rocket multidisciplinary design optimization problem to illustrate its effectiveness and practicality for solving real-world engineering problems.

黑盒计算密集型目标和约束条件下的设计优化问题在工程实践中极具挑战性。为了解决这个问题，提出了一种有效的基于元模型的优化策略，该策略使用了带有约束聚合的并行自适应克里格方法（PAKM-CA）。在PAKM-CA中，使用Kreisselmeier和Steinhauser（KS）函数汇总了复杂的昂贵约束。此外，基于客观最优和KS函数可行性的帕累托非支配概念，开发了一种新颖的考虑样本点相关性的并行综合可行预期改进（PCFEI）函数，以有效地确定顺序填充样本点。选择具有最高PCFEI函数值的填充样本点以动态优化kriging元模型，同时提高了优化的可行性和可行性。此外，可通过PCFEI的并行采样框架进一步缩短优化时间。然后，通过在数值基准上与基于竞争的最新基于元模型的约束优化方法进行比较，证明了PAKM-CA的收敛性和效率优劣。最后，将PAKM-CA应用于实际的远程细长型制导火箭弹多学科设计优化问题，以说明其解决实际工程问题的有效性和实用性。然后，通过在数值基准上与基于竞争的最新基于元模型的约束优化方法进行比较，证明了PAKM-CA的收敛性和效率优劣。最后，将PAKM-CA应用于实际的远程细长型制导火箭弹多学科设计优化问题，以说明其解决实际工程问题的有效性和实用性。然后，通过在数值基准上与基于竞争的最新基于元模型的约束优化方法进行比较，证明了PAKM-CA的收敛性和效率优劣。最后，将PAKM-CA应用于实际的远程细长型制导火箭弹多学科设计优化问题，以说明其解决实际工程问题的有效性和实用性。