SDDMO-Bench：面向流数据驱动动态多目标优化的基准套件
SDDMO-Bench: A Benchmark Suite for Streaming Data-Driven Dynamic Multi-Objective Optimization
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中文总结 AI 辅助
该研究提出SDDMO-Bench基准套件，将经典动态多目标测试问题转化为流环境，构建30种场景，经实验验证其可标准化评估流数据驱动动态多目标优化算法的相关能力。
中文摘要 AI 辅助
流数据驱动动态多目标优化要求算法在概念漂移下仅利用序列观测值跟踪时变的帕累托前沿。然而，由于实际问题通常缺乏最优解的真实值、漂移标注和可控条件，且现有基准对标准化对比的支持有限，系统评估仍存在困难。本文提出SDDMO-Bench，一款基准套件，它结合内在目标映射演化、可控分布漂移和序列数据揭示，将经典动态多目标测试问题转化为流环境。通过结合5种代表性时变基函数与6种分布漂移模式，SDDMO-Bench构建了30种场景，涵盖不同程度的非平稳性、问题复杂度、样本分布变化及帕累托前沿演化。对代表性进化算法的实验表明，SDDMO-Bench提供了具有挑战性和区分度的测试场景，为评估流数据驱动动态多目标优化中的适应性、鲁棒性和帕累托前沿跟踪能力提供了标准化、可控且可复现的基准。
英文摘要
Streaming data-driven dynamic multi-objective optimization requires algorithms to track time-varying Pareto fronts using only sequential observations under concept drift. However, systematic evaluation remains difficult because real-world problems usually lack ground-truth optima, drift annotations, and controllable conditions, while existing benchmarks provide limited support for standardized comparison. This paper proposes SDDMO-Bench, a benchmark suite that transforms classical dynamic multi-objective test problems into streaming environments by combining intrinsic objective-mapping evolution, controllable distributional drift, and sequential data revelation. By combining five representative time-dependent base functions with six distributional drift patterns, SDDMO-Bench constructs 30 scenarios with diverse levels of non-stationarity, problem complexity, sample-distribution variation, and Pareto-front evolution. Experiments with representative evolutionary algorithms demonstrate that SDDMO-Bench provides challenging and discriminative test scenarios, offering a standardized, controllable, and reproducible benchmark for evaluating adaptability, robustness, and Pareto-front tracking in streaming data-driven dynamic multi-objective optimization.
