Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective optimisation problems, the bi-objective permutation flowshop and travelling salesman problems. Our results show that multi-objective algorithms are indeed best configured using a multi-objective configurator.

中文翻译：

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置换问题的多目标局部搜索算法的自动配置

自动算法配置 (AAC) 正在成为设计高性能求解器以解决具有挑战性的优化问题的关键因素。然而，关于 AAC 的大多数现有工作都涉及优化给定单目标算法的单个性能指标的配置程序。当然，这些配置器也可用于优化多目标算法的性能，如单个性能指标所衡量的那样。在这项工作中，我们证明了通过使用原生的多目标算法配置程序可以获得更好的结果。具体来说，我们比较了三种 AAC 方法：一种只考虑超成交量指标，第二种优化超成交量和点差的加权总和，第三种同时优化这些互补指标，使用真正的多目标方法。我们通过将这些方法应用于两个广泛研究的多目标优化问题、双目标置换流水车间和旅行商问题的高参数局部搜索框架来评估这些方法。我们的结果表明，多目标算法确实最好使用多目标配置器进行配置。