论文标题
自动构建并行算法投资组合用于多目标优化
Automatic Construction of Parallel Algorithm Portfolios for Multi-objective Optimization
论文作者
论文摘要
人们普遍观察到，在所有可能的多目标优化问题（MOPS）上，没有通用最佳的多目标进化算法（MOEA）主导所有其他MOEAS。在这项工作中，我们提倡使用并行算法投资组合（PAP），该算法（PAP）并行地独立运行多个MoeAs并获得了最佳功能，以结合不同MOEAS的优势。由于PAPS的手动构造是非平凡且乏味的，因此我们建议自动构建高性能PAP来解决拖把。具体而言，我们首先提出了一种PAP的变体，即MoeAs/Pap，可以更好地确定MOPS设置的输出解决方案，而不是常规PAP。然后，我们使用一种新型的性能指标提出了一种自动构造方法，用于评估MOEAS跨多个MOP的性能。最后，我们使用所提出的方法基于训练板的训练集和由NSGA-II的几种变体定义的算法配置空间来构建MOEAS/PAP。实验结果表明，自动构建的MOEAS/PAP甚至可以与人类专家设计的最新合奏MOEAS匹配，这表明在多目标优化中自动构造PAP的巨大潜力。
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.
