For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

中文翻译：

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约束多目标优化问题的自适应分配约束处理技术

对于解决受约束的多目标优化问题 (CMOP)，有效的约束处理技术 (CHT) 非常重要。最近，已经提出了许多用于解决 CMOP 的 CHT。但是，没有任何一种 CHT 可以胜过所有类型的 CMPOP。本文提出了一种算法，即 ACHT-M2M，它在 M2M 框架中自适应地分配现有的 CHT，以解决 CMOP。更具体地说，一个 CMOP 首先由 ACHT-M2M 分解为几个有约束的多目标优化子问题。每个子问题在一个子区域中都有一个子种群。每个子区域的 CHT 根据提出的复合性能度量自适应地分配。下一代的种群由具有不同 CHT 的选择算子从子区域中选择，并且在每一代中获得的非支配可行解用于更新预定义的存档。ACHT-M2M集合了不同CHT的优势，使它们相互配合。最后将所提出的 ACHT-M2M 与基准 CMOPs 上的其他 12 种代表性算法进行了比较，实验结果进一步证实了 ACHT-M2M 解决 CMOPs 的有效性。