The optimization problems in realistic world present significant challenges onto optimization algorithms, such as the expensive evaluation issue and complex constraint conditions. COBRA optimizer (including its up-to-date variants) is a representative and effective tool for addressing such optimization problems, which introduces 1) RBF surrogate to reduce online evaluation and 2) bi-stage optimization process to alternate search for feasible solution and optimal solution. Though promising, its design space, i.e., surrogate model pool and selection standard, is still manually decided by human expert, resulting in labor-intensive fine-tuning for novel tasks. In this paper, we propose a learning-based adaptive strategy (COBRA++) that enhances COBRA in two aspects: 1) An augmented surrogate pool to break the tie with RBF-like surrogate and hence enhances model diversity and approximation capability; 2) A reinforcement learning-based online model selection policy that empowers efficient and accurate optimization process. The model selection policy is trained to maximize overall performance of COBRA++ across a distribution of constrained optimization problems with diverse properties. We have conducted multi-dimensional validation experiments and demonstrate that COBRA++ achieves substantial performance improvement against vanilla COBRA and its adaptive variant. Ablation studies are provided to support correctness of each design component in COBRA++.

翻译：现实世界中的优化问题对优化算法提出了重大挑战，例如昂贵评估问题与复杂约束条件。COBRA优化器（包括其最新变体）是解决此类优化问题的代表性有效工具，其引入：1）RBF代理模型以减少在线评估开销；2）双阶段优化流程以交替搜索可行解与最优解。尽管前景广阔，其设计空间（即代理模型池与选择标准）仍由人类专家手动决定，导致针对新任务需进行劳动密集型的精细调参。本文提出一种基于学习的自适应策略（COBRA++），从两方面增强COBRA：1）构建增强型代理池以突破类RBF代理模型的局限，从而提升模型多样性与近似能力；2）设计基于强化学习的在线模型选择策略，以实现高效精准的优化流程。该模型选择策略通过训练，可在具有不同特性的约束优化问题分布上最大化COBRA++的整体性能。我们开展了多维验证实验，证明COBRA++相较于原始COBRA及其自适应变体均取得显著性能提升。消融实验验证了COBRA++中各设计组件的有效性。