用正则化方法改进多目标贝叶斯优化，显著减少飞机设计函数评估次数。
Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
- 引入正则化策略解决多目标填充准则的病态问题
- 在飞机设计中将函数评估次数减少20倍
- 适合需要少采样的复杂工程优化场景
贝叶斯优化是一种高效全局优化工具，通过迭代构建目标函数和约束的代理模型（如Kriging模型）来求解计算成本高昂的问题。当前高效处理昂贵型多目标优化问题的扩展方法备受关注。本文将超高效全局优化混合专家模型（SEGOMOE）拓展至含约束的多目标优化。针对多目标填充准则的病态性，提出多种基于正则化技术的更新策略。在多个带约束与无约束的多目标基准测试问题上验证了所提方法的有效性。进一步应用于包含5个设计变量和3个非线性不等式约束的双目标飞机概念设计问题，初步结果表明，相较进化算法NSGA-II，函数评估总成本降低了20倍。
原文摘要 · Abstract (English)
Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints both supposed to be computationally expensive of the targeted optimization problem Nowadays efficient extensions of Bayesian optimization to solve expensive multiobjective problems are of high interest The proposed method in this paper extends the super efficient global optimization with mixture of experts SEGOMOE to solve constrained multiobjective problems To cope with the illposedness of the multiobjective inll criteria different enrichment procedures using regularization techniques are proposed The merit of the proposed approaches are shown on known multiobjective benchmark problems with and without constraints The proposed methods are then used to solve a biobjective application related to conceptual aircraft design with ve unknown design variables and three nonlinear inequality constraints The preliminary results show a reduction of the total cost in terms of function evaluations by a factor of 20 compared to the evolutionary algorithm NSGA-II.
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