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 language | English |
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Title of host publication | 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Electronic) | 978-1-7281-6929-3 |
ISBN (Print) | 978-1-7281-6930-9 |
DOIs | |
Publication status | Published - Jul 2020 |
Externally published | Yes |
Event | IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual Event Duration: 19 Jul 2020 → 24 Jul 2020 |
Conference
Conference | IEEE Congress on Evolutionary Computation, CEC 2020 |
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Abbreviated title | CEC 2020 |
City | Virtual Event |
Period | 19/07/20 → 24/07/20 |
Keywords
- Anytime behavior
- Automated algorithm selection
- Hybridization
- Performance assessment
- Traveling salesperson problem
- n/a OA procedure