Property analysis of symmetric travelling salesman problem instances acquired through evolution

TL;DR

This paper demonstrates how an evolutionary algorithm can generate challenging symmetric TSP instances and analyzes their structural properties to understand the efficiency of different solving algorithms.

Contribution

It introduces a method to evolve difficult TSP instances and provides insights into how structural properties affect algorithm performance.

Findings

Evolutionary algorithm successfully creates hard TSP instances.

Structural properties influence the efficiency of Lin-Kernighan variants.

Analysis guides understanding of algorithm performance based on instance features.

Abstract

We show how an evolutionary algorithm can successfully be used to evolve a set of difficult to solve symmetric travelling salesman problem instances for two variants of the Lin-Kernighan algorithm. Then we analyse the instances in those sets to guide us towards deferring general knowledge about the efficiency of the two variants in relation to structural properties of the symmetric travelling sale sman problem.