Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection

Jakob Bossek, Pascal Kerschke, Heike Trautmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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 close-to 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.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationGlasgow, UK
PublisherIEEE EDS
Number of pages8
ISBN (Electronic)978-1-7281-6929-3
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventIEEE Congress on Evolutionary Computation, CEC 2020 - Virtual Event
Duration: 19 Jul 202024 Jul 2020

Conference

ConferenceIEEE Congress on Evolutionary Computation, CEC 2020
Abbreviated titleCEC 2020
CityVirtual Event
Period19/07/2024/07/20

Fingerprint Dive into the research topics of 'Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection'. Together they form a unique fingerprint.

Cite this