State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
Bielefeld University, 33619 Bielefeld, Germany State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
Evol Comput. 2023 Dec 1;31(4):433-458. doi: 10.1162/evco_a_00332.
Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes a knowledge-transfer-based data-driven optimization algorithm to address these issues. First, an ensemble learning method is adopted to train surrogate models to leverage the knowledge of data in historical environments as well as adapt to new environments. Specifically, given data in a new environment, a model is constructed with the new data, and the preserved models of historical environments are further trained with the new data. Then, these models are considered to be base learners and combined as an ensemble surrogate model. After that, all base learners and the ensemble surrogate model are simultaneously optimized in a multitask environment for finding optimal solutions for real fitness functions. In this way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the ensemble model is the most accurate surrogate, we assign more individuals to the ensemble surrogate than its base learners. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art offline data-driven optimization algorithms. Code is available at https://github.com/Peacefulyang/DSE_MFS.git.
现有的离线数据驱动优化工作主要集中在静态环境中的问题上，而对动态环境中的问题关注较少。动态环境中的离线数据驱动优化是一个具有挑战性的问题，因为收集的数据的分布随时间变化，需要跟踪时间的代理模型和最优解。本文提出了一种基于知识迁移的数据驱动优化算法来解决这些问题。首先，采用集成学习方法训练代理模型，以利用历史环境中数据的知识，并适应新环境。具体来说，对于新环境中的数据，使用新数据构建模型，并使用新数据进一步训练历史环境中保存的模型。然后，这些模型被视为基学习者，并结合起来作为一个集成代理模型。之后，在多任务环境中同时优化所有基学习者和集成代理模型，以找到真实适应度函数的最优解。通过这种方式，可以利用以前环境中的优化任务来加速当前环境中最优解的跟踪。由于集成模型是最准确的代理，我们为集成代理分配的个体比其基学习者多。在六个动态优化基准问题上的实验结果表明，与四种最先进的离线数据驱动优化算法相比，所提出的算法是有效的。代码可在 https://github.com/Peacefulyang/DSE_MFS.git 获得。
