国防科技大学学报2025，Vol.47Issue(5)：125-133,9.DOI:10.11887/j.issn.1001-2486.23020012
黑箱模型约束动态松弛的近似优化方法
Approximate optimization method for constraints dynamic relaxation of black box model
摘要
Abstract
The surrogate model-based optimization method provides an effective technical approach for the application of high-precision simulation models in optimal design due to its efficient search capability.To address the problem of time-consuming black-box constraint processing for optimization problems,a multi-constraint adaptive sampling method based on improved feasible rules was proposed,an elite archive-driven inexact search method and an ε-constraint-preserving pseudo-feasible domain construction method were established,and the algorithm's ability to explore the boundaries of the feasible domain was enhanced by dynamically scaling the feasible domain to accept high-quality nonfeasible samples during the iterative process,which improved the surrogate model-based optimization search ability.Simulation results of the Congress on Evolutionary Computation constraint optimization standard function indicate that the ε-constraint maintenance optimization method is effective in solving the multi-constraint surrogate model optimization problem compared with the existing methods.The results for the solid rocket motor rear wing pillar charge design show that the algorithm has the potential to be applied to complex engineering problems.关键词
代理模型/约束处理/自适应采样/ε-约束保持Key words
surrogate model/constraint processing/adaptive sampling/ε-constraint holding分类
航空航天引用本文复制引用
马帅超,武泽平,杨家伟,高经纬..黑箱模型约束动态松弛的近似优化方法[J].国防科技大学学报,2025,47(5):125-133,9.基金项目
国家自然科学基金资助项目(52005502) （52005502）
