Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for conventional evolutionary algorithms (EAs) due to the existence of multiple constraints and objectives. When the number of objectives or decision variables is scaled up in CMOPs, the performance of EAs may degenerate dramatically and may fail to obtain any feasible solutions. To address this issue, we propose a paired offspring generation-based multiobjective EA for constrained large-scale optimization. The general idea is to emphasize the role of offspring generation in reproducing some promising feasible or useful infeasible offspring solutions. We first adopt a small set of reference vectors for constructing several subpopulations with a fixed number of neighborhood solutions. Then, a pairing strategy is adopted to determine some pairwise parent solutions for offspring generation. Consequently, the pairwise parent solutions, which could be infeasible, may guide the generation of well-converged solutions to cross the infeasible region(s) effectively. The proposed algorithm is evaluated on CMOPs with up to 1000 decision variables and ten objectives. Moreover, each component in the proposed algorithm is examined in terms of its effect on the overall algorithmic performance. Experimental results on a variety of existing and our tailored test problems demonstrate the effectiveness of the proposed algorithm in constrained large-scale multiobjective optimization.

中文翻译：

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用于约束大规模多目标优化的配对后代生成


约束多目标优化问题（CMOP）广泛存在于现实应用中，由于存在多个约束和目标，它们对传统进化算法（EA）提出了挑战。当 CMOP 中的目标或决策变量的数量扩大时，EA 的性能可能会急剧下降，并且可能无法获得任何可行的解决方案。为了解决这个问题，我们提出了一种基于配对后代生成的多目标 EA，用于约束大规模优化。总体思想是强调后代生成在复制一些有希望的可行或有用的不可行后代解决方案中的作用。我们首先采用一小组参考向量来构建具有固定数量邻域解的多个子群。然后，采用配对策略来确定后代生成的一些配对父解。因此，可能不可行的成对父解可以指导生成良好收敛的解以有效地跨越不可行区域。所提出的算法在具有多达 1000 个决策变量和 10 个目标的 CMOP 上进行评估。此外，还检查了所提出算法中的每个组件对整体算法性能的影响。各种现有的和我们定制的测试问题的实验结果证明了所提出的算法在约束大规模多目标优化中的有效性。