Anytime behavior of inexact TSP solvers and perspectives for automated algorithm selection

Part of: IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural Networks (IJCNN 2020); the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020); and the 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020).The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate closeto optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.Jakob Bossek, Pascal Kerschke, Heike Trautman