In recent years, there has been a growing interest in data-driven evolutionary algorithms (DDEAs) employing surrogate models to approximate the objective functions with limited data. However, current DDEAs are primarily designed for lower-dimensional problems and their performance drops significantly when applied to large-scale optimization problems (LSOPs). To address the challenge, this paper proposes an offline DDEA named DSKT-DDEA. DSKT-DDEA leverages multiple islands that utilize different data to establish diverse surrogate models, fostering diverse subpopulations and mitigating the risk of premature convergence. In the intra-island optimization phase, a semi-supervised learning method is devised to fine-tune the surrogates. It not only facilitates data argumentation, but also incorporates the distribution information gathered during the search process to align the surrogates with the evolving local landscapes. Then, in the inter-island knowledge transfer phase, the algorithm incorporates an adaptive strategy that periodically transfers individual information and evaluates the transfer effectiveness in the new environment, facilitating global optimization efficacy. Experimental results demonstrate that our algorithm is competitive with state-of-the-art DDEAs on problems with up to 1000 dimensions, while also exhibiting decent parallelism and scalability. Our DSKT-DDEA is open-source and accessible at: https://github.com/LabGong/DSKT-DDEA.

翻译：近年来，采用代理模型在有限数据下近似目标函数的数据驱动进化算法日益受到关注。然而，现有的数据驱动进化算法主要面向低维问题设计，在处理大规模优化问题时性能显著下降。为应对这一挑战，本文提出一种名为DSKT-DDEA的离线数据驱动进化算法。该算法利用多个采用不同数据构建多样化代理模型的岛屿，培育多样化子种群，从而降低早熟收敛风险。在岛内优化阶段，设计了一种半监督学习方法对代理模型进行微调。该方法不仅实现了数据增强，还结合搜索过程中收集的分布信息，使代理模型与动态演化的局部搜索空间特征保持同步。随后，在岛间知识迁移阶段，算法引入自适应策略，周期性迁移个体信息并在新环境中评估迁移效果，从而提升全局优化效能。实验结果表明，在维度高达1000的优化问题上，本算法与最先进的数据驱动进化算法相比具有竞争力，同时展现出良好的并行性与可扩展性。DSKT-DDEA算法已开源，访问地址为：https://github.com/LabGong/DSKT-DDEA。