用AI自动选分解策略，让优化算法更智能高效
Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization
- 用神经网络动态选择变量分解策略
- 在多个问题上优于现有方法，节省资源
- 能迁移到未见过的问题，适合大规模优化
近年来，协同进化（Cooperative Coevolution, CC）在解决大规模全局优化问题上取得显著进展。然而，现有CC范式主要受限于需依赖深厚专业知识来选择或设计有效的变量分解策略。受元黑箱优化进展启发，本文提出LCC——一种开创性的基于学习的协同进化框架，可在优化过程中动态调度分解策略。分解策略选择器通过神经网络参数化，利用精心设计的优化状态特征集，为每一步优化决策最优策略。该网络通过近端策略优化方法，在一组代表性问题上以强化学习方式训练，目标是最大化期望优化性能。大量实验表明，LCC不仅在优化效果和资源消耗方面优于当前最优基线，还展现出对未见问题的良好可迁移性。
原文摘要 · Abstract (English)
Recent research in Cooperative Coevolution~(CC) have achieved promising progress in solving large-scale global optimization problems. However, existing CC paradigms have a primary limitation in that they require deep expertise for selecting or designing effective variable decomposition strategies. Inspired by advancements in Meta-Black-Box Optimization, this paper introduces LCC, a pioneering learning-based cooperative coevolution framework that dynamically schedules decomposition strategies during optimization processes. The decomposition strategy selector is parameterized through a neural network, which processes a meticulously crafted set of optimization status features to determine the optimal strategy for each optimization step. The network is trained via the Proximal Policy Optimization method in a reinforcement learning manner across a collection of representative problems, aiming to maximize the expected optimization performance. Extensive experimental results demonstrate that LCC not only offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption, but it also exhibits promising transferability towards unseen problems.
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