Many optimization tasks involve streaming data with unknown concept drifts, posing a significant challenge as Streaming Data-Driven Optimization (SDDO). Existing methods, while leveraging surrogate model approximation and historical knowledge transfer, are often under restrictive assumptions such as fixed drift intervals and fully environmental observability, limiting their adaptability to diverse dynamic environments. We propose TRACE, a TRAnsferable Concept-drift Estimator that effectively detects distributional changes in streaming data with varying time scales. TRACE leverages a principled tokenization strategy to extract statistical features from data streams and models drift patterns using attention-based sequence learning, enabling accurate detection on unseen datasets and highlighting the transferability of learned drift patterns. Further, we showcase TRACE's plug-and-play nature by integrating it into a streaming optimizer, facilitating adaptive optimization under unknown drifts. Comprehensive experimental results on diverse benchmarks demonstrate the superior generalization, robustness, and effectiveness of our approach in SDDO scenarios.

Dynamic Optimization Problems (DOPs) are challenging to address due to their complex nature, i.e., dynamic environment variation. Evolutionary Computation methods are generally advantaged in solving DOPs since they resemble dynamic biological evolution. However, existing evolutionary dynamic optimization methods rely heavily on human-crafted adaptive strategy to detect environment variation in DOPs, and then adapt the searching strategy accordingly. These hand-crafted strategies may perform ineffectively at out-of-box scenarios. In this paper, we propose a reinforcement learning-assisted approach to enable automated variation detection and self-adaption in evolutionary algorithms. This is achieved by borrowing the bi-level learning-to-optimize idea from recent Meta-Black-Box Optimization works. We use a deep Q-network as optimization dynamics detector and searching strategy adapter: It is fed as input with current-step optimization state and then dictates desired control parameters to underlying evolutionary algorithms for next-step optimization. The learning objective is to maximize the expected performance gain across a problem distribution. Once trained, our approach could generalize toward unseen DOPs with automated environment variation detection and self-adaption. To facilitate comprehensive validation, we further construct an easy-to-difficult DOPs testbed with diverse synthetic instances. Extensive benchmark results demonstrate flexible searching behavior and superior performance of our approach in solving DOPs, compared to state-of-the-art baselines. Our code is publicly available here: https://github.com/MetaEvo/Meta-DO

In recent years, the data-driven optimization area has seen a shift in the research focus from static batched data environment to dynamic streaming data environment. However, this field is hindered by the lack of a comprehensive and standardized test suite. To fill this gap, we introduce SDDObench, the first benchmark tailored for evaluating and comparing the streaming data-driven evolutionary algorithms (SDDEAs). SDDObench comprises two sets of objective functions combined with five different types of concept drifts, which offer the benefit of being inclusive in generating data streams that mimic various real-world situations, while also facilitating straight-forward description and analysis. As a proof-of-concept study, four well-known algorithms are selected to tackle the problems generated by SDDObench. The experiment results and analysis reveal ongoing challenges in attaining good performance for streaming data-driven optimization. Our SDDObench is open-source and accessible at: https://github.com/LabGong/SDDObench.

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.