无需预设任务，模型可自适应优化复杂黑箱问题。
Task-free Adaptive Meta Black-box Optimization
- 在线学习目标任务数据，动态调整优化参数。
- 零样本下在合成与真实任务中表现媲美人工设计方法。
- 适合未知任务分布的实时优化场景，如无人机路径规划。
手工设计的优化器在复杂黑箱优化（BBO）任务中效率低下。元黑箱优化（MetaBBO）通过元学习自动配置低层优化器，减少对经验规则的依赖。但现有方法通常需要大量手工设计的训练任务来学习泛化策略，这对任务分布未知的实际应用构成重大限制。为此，我们提出自适应元黑箱优化模型（ABOM），仅利用目标任务的优化数据实现在线参数自适应，无需预定义任务分布。不同于传统元BBO将元训练与优化阶段分离，ABOM引入闭环自适应参数学习机制，参数化进化算子通过优化过程中生成的种群持续自我更新。这一范式转变实现了零样本优化：在合成BBO基准和真实的无人飞行器路径规划问题上，ABOM无需任何手工训练任务即可达到竞争性性能。可视化研究显示，参数化进化算子表现出显著的搜索模式，包括自然选择与基因重组。
原文摘要 · Abstract (English)
Handcrafted optimizers become prohibitively inefficient for complex black-box optimization (BBO) tasks. MetaBBO addresses this challenge by meta-learning to automatically configure optimizers for low-level BBO tasks, thereby eliminating heuristic dependencies. However, existing methods typically require extensive handcrafted training tasks to learn meta-strategies that generalize to target tasks, which poses a critical limitation for realistic applications with unknown task distributions. To overcome the issue, we propose the Adaptive meta Black-box Optimization Model (ABOM), which performs online parameter adaptation using solely optimization data from the target task, obviating the need for predefined task distributions. Unlike conventional metaBBO frameworks that decouple meta-training and optimization phases, ABOM introduces a closed-loop adaptive parameter learning mechanism, where parameterized evolutionary operators continuously self-update by leveraging generated populations during optimization. This paradigm shift enables zero-shot optimization: ABOM achieves competitive performance on synthetic BBO benchmarks and realistic unmanned aerial vehicle path planning problems without any handcrafted training tasks. Visualization studies reveal that parameterized evolutionary operators exhibit statistically significant search patterns, including natural selection and genetic recombination.
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