Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach
提出一种协同进化数据驱动算法，通过分层代理模型和梯度算子解决大规模优化中的维度灾难问题，在高达1000维问题上验证了有效性。
Data-driven evolutionary algorithms (DDEAs) have received increasing attention during the past decade, but most existing studies are dedicated to solving relatively small-scale problems. For large-scale optimization problems (LSOPs), special efforts must be made to both the surrogate and the evolutionary components to overcome the “curse of dimensionality”, which remains a challenge in this research area. To address this research limitation, we propose a novel cooperative coevolution-based DDEA (CC-DDEA). First, a hierarchical surrogate-joint learning model is designed to provide fitness approximations at both global and subdivided spaces, thus being able to guide the evolutionary population searching at different granularities. Then, optimization is conducted on both the global level and local sub-space level in the manner of cooperative coevolution. In the local-level search, we introduce a gradient-based operator to accelerate the convergence efficiency of sub-spaces, owing to the differentiable property of our surrogate model. Additionally, the entire framework is used in conjunction with a progressive and dynamic space division strategy, enabling local parallel-to-global unified search and facilitating the final convergence. Experiments on up to 1000-dimensional problems and the comparisons with state-of-the-art DDEAs validate the powerfulness of the proposed algorithm.