A variety of meta-heuristics have shown promising performance for solving multi-objective optimization problems (MOPs). However, existing meta-heuristics may have the best performance on particular MOPs, but may not perform well on the other MOPs. To improve the cross-domain ability, this paper presents a multi-objective hyper-heuristic algorithm based on adaptive epsilon-greedy selection (HH_EG) for solving MOPs. To select and combine low-level heuristics (LLHs) during the evolutionary procedure, this paper also proposes an adaptive epsilon-greedy selection strategy. The proposed hyper-heuristic can solve problems from varied domains by simply changing LLHs without redesigning the high-level strategy. Meanwhile, HH_EG does not need to tune parameters, and is easy to be integrated with various performance indicators. We test HH_EG on the classical DTLZ test suite, the IMOP test suite, the many-objective MaF test suite, and a test suite of a real-world multi-objective problem. Experimental results show the effectiveness of HH_EG in combining the advantages of each LLH and solving cross-domain problems.

多种元启发式方法已显示出解决多目标优化问题（MOP）的有希望的性能。但是，现有的元启发式方法在特定的MOP上可能具有最佳性能，但在其他MOP上可能无法很好地执行。为了提高跨域能力，本文提出了一种基于自适应ε贪婪选择（HH_EG）的多目标超启发式算法来求解MOP。为了在进化过程中选择和组合低级启发式算法，本文还提出了一种自适应的ε贪婪选择策略。所提出的超启发式方法可以通过简单地更改LLH而不用重新设计高级策略来解决来自不同领域的问题。同时，HH_EG不需要调整参数，并且易于与各种性能指标集成。我们在经典DTLZ测试套件，IMOP测试套件，多目标MaF测试套件和真实多目标问题的测试套件上测试HH_EG。实验结果表明，HH_EG在结合每个LLH的优点和解决跨域问题方面是有效的。