动态选择优化器，提升异构大规模优化的精度与效率。
A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization
- 用元智能体按需选最优优化器，自适应处理不同子问题。
- 在3000维复杂耦合问题上，解的质量和效率均优于现有方法。
- 对不同问题、时长和优化器都有强泛化能力，适合真实场景。
协同进化（CC）通过分解有效解决大规模全局优化（LSGO），但在现实应用中出现的异构大规模优化（H-LSGO）问题——子问题维度多样、结构差异大——使其表现受限。现有基于固定低维优化器的范式难以应对这种异质性。为此，本文提出基于学习的异构协同进化框架（LH-CC）。将优化过程建模为马尔可夫决策过程，由元智能体动态选择最适配的优化器。同时构建灵活基准套件，生成多样化H-LSGO实例。在3000维、具复杂耦合关系的问题上，实验表明LH-CC在解质量与计算效率上均显著优于主流基线。框架还展现出对不同问题实例、优化时长及优化器的鲁棒泛化能力。结果揭示：动态优化器选择是解决复杂H-LSGO的关键策略。
原文摘要 · Abstract (English)
Cooperative Coevolution (CC) effectively addresses Large-Scale Global Optimization (LSGO) via decomposition but struggles with the emerging class of Heterogeneous LSGO (H-LSGO) problems arising from real-world applications, where subproblems exhibit diverse dimensions and distinct landscapes. The prevailing CC paradigm, relying on a fixed low-dimensional optimizer, often fails to navigate this heterogeneity. To address this limitation, we propose the Learning-Based Heterogeneous Cooperative Coevolution Framework (LH-CC). By formulating the optimization process as a Markov Decision Process, LH-CC employs a meta-agent to adaptively select the most suitable optimizer for each subproblem. We also introduce a flexible benchmark suite to generate diverse H-LSGO problem instances. Extensive experiments on 3000-dimensional problems with complex coupling relationships demonstrate that LH-CC achieves superior solution quality and computational efficiency compared to state-of-the-art baselines. Furthermore, the framework exhibits robust generalization across varying problem instances, optimization horizons, and optimizers. Our findings reveal that dynamic optimizer selection is a pivotal strategy for solving complex H-LSGO problems.
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