Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel classification tree based adaptive operator selection strategy for multi-objective evolutionary algorithm based on decomposition (MOEA/D-CTAOS). In our proposal, the classification tree is trained by the recorded data set which contains the information on the historical offspring. Before the reproduction at each generation, the classifier is used to predict each possible result obtained by different operators, and only one operator with the best result is selected to generate offspring next. Meanwhile, a novel differential evolution based on search inertia (SiDE) is designed to steer the evolutionary process in a more efficient way. The experimental results demonstrate that proposed MOEA/D-CTAOS outperforms other MOEA/D variants on UF and LZ benchmarks in terms of IGD and HV value. Further investigation also confirms the advantage of direction-guided search strategy in SiDE.

自适应算子选择（AOS）用于动态选择合适的基因算子进行后代繁殖，旨在通过在进化过程中产生高质量的后代来提高进化算法（EA）的性能。针对基于分解的多目标进化算法（MOEA/D-CTAOS），提出了一种新的基于分类树的自适应算子选择策略。在我们的提议中，分类树由包含历史后代信息的记录数据集进行训练。在每一代的再现之前，通过分类器来预测不同算子得到的每个可能的结果，只选择结果最好的一个算子产生下一个子代​​。同时，基于搜索惯性（SiDE）的新型差分进化旨在以更有效的方式引导进化过程。实验结果表明，在 IGD 和 HV 值方面，提出的 MOEA/D-CTAOS 在 UF 和 LZ 基准上优于其他 MOEA/D 变体。进一步的调查也证实了方向引导搜索策略在 SiDE 中的优势。