基于强化学习的约束松弛在受约束的昂贵优化中的应用
Meta-Learning-Assisted Constraint Relaxation for Constrained Black-Box Optimization
AI总结：
本文提出通过强化学习学习有效的约束处理策略，以提高受约束昂贵优化问题的求解性能。
摘要：
在解决现实中的复杂优化问题时，约束处理起着关键作用。尽管在过去的几十年里，现有的约束处理技术主要依赖于人类专家的设计，这在一般情况下或多或少地无法满足需求。受最近在元黑箱优化中的进展启发，其中自动化算法设计可以被学习以提高优化性能，在本文中，我们提出通过强化学习学习有效的、适应性强且具有通用性的约束处理策略。具体来说，首先制定了一个定制的马尔可夫决策过程，在此过程中，给定优化动态特征，基于深度Q网络的策略控制约束松弛水平在整个优化过程中。这种自适应的约束处理提供了在目标导向的利用和可行区域导向的探索之间的灵活权衡，因此导致了有希望的优化性能。我们在CEC 2017受约束优化基准上训练我们的方法，在有限的评估预算条件下（昂贵的情况）进行训练，并将训练后的约束处理策略与强大的基线进行比较，例如最近CEC/GECCO竞赛的获胜者。广泛的实验结果表明，我们的方法在留一法交叉验证或普通训练-测试分割验证下表现竞争性，甚至超过了比较的基线。进一步的分析和消融研究揭示了我们设计中的关键见解。
英文摘要：
Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often provide conflicting search signals. Existing $ε$-relaxation methods are simple and effective, but their relaxation schedules are usually fixed or manually designed for a limited range of problems. To address this limitation, this letter proposes MeCO, a meta-learning-assisted optimizer that learns an adaptive $ε$-relaxation policy for constrained BBO. MeCO couples a SHADE optimizer with a Double Deep Q-Network controller. At each optimization step, the controller observes compact population and constraint features and selects a scalar action, which is decoded into a relaxation vector for the candidate comparison rule. The policy is trained across constrained BBO instances and then deployed on held-out problems without problem-specific tuning. Experiments on the CEC2017 constrained benchmark, 16 UAV path-planning tasks and eight real-world engineering problems provide evidence that MeCO transfers across held-out benchmark functions, higher dimensions, and an application-domain setting. Ablation and behavior analyses further clarify the roles of constraint-related state features, action scaling, reward shaping, and meta-training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
