Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference

摘要

State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem (TSP) are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.