统一球面黑箱优化方法，设计可调混合算法提升性能与鲁棒性。
Bridging Spherical Black-Box Optimizers
- 通过融合适应度聚合与共识范围，统一多种黑箱优化方法
- 混合算法在连续控制任务中实现性能与鲁棒性的可控权衡
- 适合需高效多模态搜索的高维优化场景
当梯度信息不可用时，黑箱优化（BBO）提供实用替代方案。尽管进化策略（ES）、基于共识的优化（CBO）、积分优化（OVI）等方法各自独立研究，但其关联尚未充分探索。本文在统一理论框架下整合这些方法，揭示其差异主要源于两个设计选择：适应度聚合（控制尖峰偏好）和共识范围（控制模态）。基于此，提出可插值的混合优化器。ES-OVI混合器可显式控制对平坦极小值的偏好，在连续控制任务中实现性能与鲁棒性的权衡。CBO-OVI混合器结合参数化方法的高维效率与粒子方法的多模态能力，在有限评估预算下于语言模型合并任务中达到竞争力结果。我们在标准BBO基准与高维运动控制任务上验证，混合方法优于其组成算法。
原文摘要 · Abstract (English)
When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative. While Evolution Strategies (ES), Consensus-Based Optimization (CBO), Optimization via Integration (OVI), and related methods have each been studied independently, their connections remain underexplored. We unify these approaches within a common theoretical framework, revealing that they differ primarily in two design choices: fitness aggregation (controlling sharpness preference) and consensus scope (controlling modality). Leveraging these insights, we introduce hybrid optimizers that interpolate between existing methods. Our ES-OVI hybrid allows explicit control over the preference for flat minima, enabling a trade-off between performance and robustness in continuous control tasks. Our CBO-OVI hybrids combine the higher-dimensional efficiency of parametric methods with the multimodal capabilities of particle-based approaches, achieving competitive results on language model merging under limited evaluation budgets. We validate our methods on standard BBO benchmarks and higher-dimensional locomotion tasks, demonstrating that the hybrid methods can outperform their constituent algorithms.
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