论文标题
smgo- $δ$：在全球优化中平衡谨慎和奖励与黑盒约束
SMGO-$Δ$: Balancing Caution and Reward in Global Optimization with Black-Box Constraints
论文作者
论文摘要
在所有科学和工程领域的许多应用中，存在优化问题，其中目标函数和约束都没有封闭形式的表达或太复杂而无法在分析上进行管理，只能通过实验对其进行评估。为了解决此类问题，我们针对黑盒目标和约束问题的问题设计了全球优化技术。假设Lipschitz的成本和约束功能的连续性，采用设定的成员资格框架来构建优化程序的替代模型，该模型用于开发和探索例程。所得的算法，命名为具有黑盒约束（Smgo-$δ$）的Set成员全球优化，具有一个可调风险参数，用户可以直观地适应权衡安全，开发和探索。得出了算法的理论特性，并将优化性能与几种基准中文献的代表性技术进行了比较。最后，在案例研究中，对其进行了测试并与贝叶斯优化的约束优化，该案例研究与模拟具有干扰和植物不确定性的伺服力学的预测控制调整有关，以解决实际动机的任务级别的约束。
In numerous applications across all science and engineering areas, there are optimization problems where both the objective function and the constraints have no closed-form expression or are too complex to be managed analytically, that they can only be evaluated through experiments. To address such issues, we design a global optimization technique for problems with black-box objective and constraints. Assuming Lipschitz continuity of the cost and constraint functions, a Set Membership framework is adopted to build a surrogate model of the optimization program, that is used for exploitation and exploration routines. The resulting algorithm, named Set Membership Global Optimization With Black-Box Constraints (SMGO-$Δ$), features one tunable risk parameter, which the user can intuitively adjust to trade-off safety, exploitation, and exploration. The theoretical properties of the algorithm are derived, and the optimization performance is compared with representative techniques from the literature in several benchmarks. Lastly, it is tested and compared with constrained Bayesian optimization in a case study pertaining to model predictive control tuning for a servomechanism with disturbances and plant uncertainties, addressing practically-motivated task-level constraints.
