A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization
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 ...
| Authors | Wenjie Qiu, Zixin Wang, Hongyu Fang et al. |
|---|---|
| Primary category | cs.NE |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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