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：众所周知，不存在一个通用的最优多目标进化算法（MOEA），能够在所有可能的多目标优化问题（MOP）上优于其他所有MOEA。本研究主张采用并行算法库（PAP），即并行独立运行多个MOEA并从中选出最优结果，以整合不同MOEA的优势。由于人工构建PAP既困难又繁琐，我们提出自动构建高性能PAP以求解MOP的方法。具体而言，我们首先提出PAP的一种变体——MOEAs/PAP，相较于传统PAP能更优地确定MOP的输出解集；随后，我们提出一种针对MOEAs/PAP的自动构建方法，该方法采用新颖的性能指标来评估MOEA在多个MOP上的表现；最后，我们以一组MOP训练集和由多种NSGA-II变体定义的算法配置空间为基础，利用所提方法构建了一个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最新集成MOEA相媲美，这充分展示了多目标优化中自动构建PAP的巨大潜力。