LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems
LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems
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DOI：
10.23919/acc55779.2023.10155821
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发表时间：
2023-05
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影响因子：
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通讯作者：
J. Paulson;Farshud Sorourifar;C. Laughman;A. Chakrabarty
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文献类型：
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作者：
J. Paulson;Farshud Sorourifar;C. Laughman;A. Chakrabarty
贝叶斯优化（BO）最近被证明是一种有效的无导数优化昂贵的黑盒函数的强大工具，例如在复杂能源系统的性能优化中普遍存在的黑盒函数。经典的BO算法忽略了连续优化器候选者之间的关系，导致在可接受的搜索空间中跳转，这可能导致触发故障安全机制，或者违反操作约束的不希望的瞬态动态。在本文中，我们提出了一种新的全局优化方法LSR- bo，它通过设计来强制执行局部搜索区域（LSR）约束，这限制了每次迭代时优化器候选值的变化程度。我们证明，天真地将LSR约束纳入BO会导致算法陷入局部次优解，并通过开发一种新的探索策略来克服这一挑战，该策略可以优雅地在短期“局部”和长期“全局”之间进行权衡，从而提高性能。并对LSR-BO的收敛性提供了理论保证。最后，我们通过一个说明性基准和一个实际的商业蒸汽压缩系统的能量最小化问题验证了我们提出的LSR-BO方法的有效性。
Bayesian optimization (BO) has recently been demonstrated as a powerful tool for efficient derivative-free optimization of expensive black-box functions, such as those prevalent in performance optimization of complex energy systems. Classical BO algorithms ignore the relationship between consecutive optimizer candidates, resulting in jumps in the admissible search space which can lead to fail-safe mechanisms being triggered, or undesired transient dynamics that violate operational constraints. In this paper, we propose LSR-BO, a novel global optimization methodology that enforces local search region (LSR) constraints by design, which restricts how much the optimizer candidate can be changed at every iteration. We demonstrate that naively incorporating LSR constraints into BO causes the algorithm to get stuck in local suboptimal solutions, and overcome this challenge through the development a novel exploration strategy that can gracefully navigate the trade-off between short-term "local", and long-term "global", performance improvement. Furthermore, we provide theoretical guarantees on the convergence of LSR-BO. Finally, we verify the effectiveness of our proposed LSR-BO method on an illustrative benchmark and a real-world energy minimization problem for a commercial vapor compression system.