Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims to design a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 constrained optimization competition benchmark suite. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimization. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective evolutionary algorithms for constrained optimization, but MOEAs based on non-dominance, such as PMODE, may not.

多目标进化算法（MOEA）已成功应用于许多约束优化问题。其中许多采用差分进化中的变异和交叉算子。然而，这些算子没有明确利用健身景观的特征。为了提高算法的性能，本文旨在设计一种适应适应度景观的搜索算子。通过观察，我们发现主成分分析（PCA）可以用来表征适应度景观。基于这一发现，提出了一种新的搜索算子，称为 PCA 投影。为了验证PCA投影的有效性，我们设计了两种用PCA投影增强的算法来解决约束优化问题，分别称为PMODE和HECO-PDE。实验已在 IEEE CEC 2017 约束优化竞赛基准套件上进行。将 PMODE 和 HECO-PDE 与 IEEE CEC 2018 竞赛的算法以及最近的另一个用于约束优化的 MOEA 算法进行了比较。实验结果表明，使用 PCA 投影增强的算法比没有该算子的相应算法表现更好。此外，根据竞赛规则，HECO-PDE在所有维度上均排名第一。这项研究表明，基于分解的 MOEA（例如 HECO-PDE）与用于约束优化的最佳单目标进化算法具有竞争力，但基于非支配性的 MOEA（例如 PMODE）可能没有竞争力。