标题： 约束多目标黑盒优化的有效搜索策略 显示英文标题

标题： Efficient search strategies for constrained multiobjective blackbox optimization

摘要： 多目标黑箱优化处理的是目标函数和约束函数由数值模拟给出的问题。在此背景下，无法获得导数，也无法通过有限差分法近似导数，这排除了使用经典梯度方法的可能性。DMulti-MADS 算法实现了一种最先进的直接搜索方法，用于基于网格自适应直接搜索（MADS）算法的多目标黑箱优化。自其提出以来，许多搜索策略被提出以提高单目标 MADS 算法的实际效率。受此先前研究的启发，本工作提出了将两种搜索启发式方法集成到 DMulti-MADS 算法中的方法。第一种方法利用从之前评估点构建的二次模型，这些模型作为真实目标和约束的代理，以建议新的有希望的候选解。第二种方法利用 Nelder-Mead 算法的采样策略来探索决策空间中的新非支配点。在解析问题和三个工程应用上的计算实验表明，使用这些搜索步骤显著提高了 DMulti-MADS 算法的性能。 显示英文摘要

摘要： Multiobjective blackbox optimization deals with problems where the objective and constraint functions are the outputs of a numerical simulation. In this context, no derivatives are available, nor can they be approximated by finite differences, which precludes the use of classical gradient-based techniques. The DMulti-MADS algorithm implements a state-of-the-art direct search procedure for multiobjective blackbox optimization based on the mesh adaptive direct search (MADS) algorithm. Since its conception, many search strategies have been proposed to improve the practical efficiency of the single-objective MADS algorithm. Inspired by this previous research, this work proposes the integration of two search heuristics into the DMulti-MADS algorithm. The first uses quadratic models, built from previously evaluated points, which act as surrogates for the true objectives and constraints, to suggest new promising candidates. The second exploits the sampling strategy of the Nelder-Mead algorithm to explore the decision space for new non-dominated points. Computational experiments on analytical problems and three engineering applications show that the use of such search steps considerably improves the performance of the DMulti-MADS algorithm.