To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel Matrix and probability model called Determinantal Point Processes (DPPs). Our Many-Objective Evolutionary Algorithm with Determinantal Point Processes (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs \textcolor{blue}{with different numbers of objectives}. The experimental results demonstrate that MaOEADPPs is competitive.

中文翻译：

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一种新的基于行列式点过程的多目标进化算法

为了处理不同类型的多目标优化问题 (MaOP)，多目标进化算法 (MaOEA) 需要同时保持高维​​目标空间中的收敛性和种群多样性。为了平衡多样性和收敛性之间的关系，我们引入了一个称为决定点过程（DPPs）的核矩阵和概率模型。提出了我们的具有行列式点过程的多目标进化算法 (MaOEADPPs)，并在各种类型的 MaOP\textcolor{blue}{具有不同数量的目标}上与几种最先进的算法进行了比较。实验结果表明 MaOEADPPs 具有竞争力。