Informed Heuristics for Guiding Stem-and-Cycle Ejection Chains

TL;DR

This paper introduces an informed heuristic for ejection chain methods in the Traveling Salesman Problem, improving solution quality by guiding successor selection with admissible heuristics, despite increased computational complexity.

Contribution

It presents a novel heuristic-guided approach for ejection chains, leveraging AI-inspired heuristics to enhance local search effectiveness in TSP.

Findings

Heuristic guidance often yields better solutions than uninformed methods.

The approach increases computational time due to higher polynomial complexity.

Empirical results demonstrate improved performance in TSP instances.

Abstract

The state of the art in local search for the Traveling Salesman Problem is dominated by ejection chain methods utilising the Stem-and-Cycle reference structure. Though effective such algorithms employ very little information in their successor selection strategy, typically seeking only to minimise the cost of a move. We propose an alternative approach inspired from the AI literature and show how an admissible heuristic can be used to guide successor selection. We undertake an empirical analysis and demonstrate that this technique often produces better results than less informed strategies albeit at the cost of running in higher polynomial time.