It has been widely observed that there exists no universal best Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs independently in parallel and gets the best out of them, to combine the advantages of different MOEAs. Since the manual construction of PAPs is non-trivial and tedious, we propose to automatically construct high-performance PAPs for solving MOPs. Specifically, we first propose a variant of PAPs, namely MOEAs/PAP, which can better determine the output solution set for MOPs than conventional PAPs. Then, we present an automatic construction approach for MOEAs/PAP with a novel performance metric for evaluating the performance of MOEAs across multiple MOPs. Finally, we use the proposed approach to construct a MOEAs/PAP based on a training set of MOPs and an algorithm configuration space defined by several variants of NSGA-II. Experimental results show that the automatically constructed MOEAs/PAP can even rival the state-of-the-art ensemble MOEAs designed by human experts, demonstrating the huge potential of automatic construction of PAPs in multi-objective optimization.

翻译：据广泛观察，不存在一种通用最优的多目标进化算法（MOEA），能在所有可能的多目标优化问题（MOP）上全面优于其他MOEA。本研究倡导使用并行算法库（PAP），即并行独立运行多个MOEA并从中选取最优结果，以融合不同MOEA的优势。由于手动构建PAP既繁琐又困难，我们提出自动构建高性能PAP以求解MOP。具体而言，我们首先提出一种PAP变体，称为MOEAs/PAP，它能比传统PAP更有效地确定MOP的输出解集。随后，我们提出一种MOEAs/PAP自动构建方法，并引入一种新颖的性能指标，用于评估MOEA在多个MOP上的整体表现。最后，我们利用该方法基于MOP训练集以及由多种NSGA-II变体定义的算法配置空间，构建了一个MOEAs/PAP。实验结果表明，自动构建的MOEAs/PAP甚至能与人类专家设计的先进集成MOEA相媲美，这展示了多目标优化中自动构建PAP的巨大潜力。