A case study of algorithm selection for the traveling thief problem

TL;DR

This paper investigates algorithm selection for the Traveling Thief Problem by creating a large dataset, defining instance features, constructing algorithm portfolios, and analyzing their contributions to improve performance.

Contribution

It introduces the first algorithm portfolios for TTP, based on extensive performance data and instance features, outperforming single algorithms.

Findings

Created a dataset of 9720 TTP instances with algorithm performance data.

Defined 55 features for TTP instances to aid algorithm selection.

Constructed algorithm portfolios that outperform the best single algorithm.

Abstract

Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.