论文标题
贝叶斯优化和用于控制工程调谐问题的米氏优化的基准和碰撞限制的基准
Benchmark of Bayesian Optimization and Metaheuristics for Control Engineering Tuning Problems with Crash Constraints
论文作者
论文摘要
基于黑框优化的控制器调整允许自动调整性能至关重要参数W.R.T.主要是任意高级闭环控制目标。但是，尚未实施针对控制工程问题的不同黑盒优化器的全面基准。因此，在这一贡献中，将11种不同版本的贝叶斯优化（BO）与十个确定性的模拟单目标调谐问题集进行了比较，将7种贝叶斯的优化（BO）与控制中的7个元启发式和其他基准进行了比较。结果表明，确定性的噪声，低模式性和实质性区域具有不可行的参数化（崩溃约束），这表征了控制工程调谐问题。因此，提出了一种使用BO处理崩溃约束的灵活方法。与标准BO相比，显示样品效率的提高。此外，基准结果表明，模式搜索（PS）在预算25 d的目标函数评估中表现最佳，并且问题维度d的d = 2 =2。Bayesian自适应直接搜索（BO和PS的组合）被证明是3 <= d <= 5的样品效率最高的，而不是随机搜索，而不是随机搜索，而不是随机搜索增加了控制器的性能，则按平均6.6％的6.6％为1.6％。
Controller tuning based on black-box optimization allows to automatically tune performance-critical parameters w.r.t. mostly arbitrary high-level closed-loop control objectives. However, a comprehensive benchmark of different black-box optimizers for control engineering problems has not yet been conducted. Therefore, in this contribution, 11 different versions of Bayesian optimization (BO) are compared with seven metaheuristics and other baselines on a set of ten deterministic simulative single-objective tuning problems in control. Results indicate that deterministic noise, low multimodality, and substantial areas with infeasible parametrizations (crash constraints) characterize control engineering tuning problems. Therefore, a flexible method to handle crash constraints with BO is presented. A resulting increase in sample efficiency is shown in comparison to standard BO. Furthermore, benchmark results indicate that pattern search (PS) performs best on a budget of 25 d objective function evaluations and a problem dimensionality d of d = 2. Bayesian adaptive direct search, a combination of BO and PS, is shown to be most sample efficient for 3 <= d <= 5. Using these optimizers instead of random search increases controller performance by on average 6.6% and up to 16.1%.
