Efficient Recombination in the Lin-Kernighan-Helsgaun Traveling Salesman Heuristic

Abstract

The Lin-Kernighan-Helsgaun (LKH) algorithm is one of the most successful search algorithms for the Traveling Salesman Problem (TSP). The core of LKH is a variable depth local search heuristic developed by Lin and Kernighan (LK). Several improvements have been incorporated to LKH along the years. The best results reported in the literature were obtained by an iterative local search version known as multi-trial LKH. In multi-trial LKH, solutions generated by soft restarts of the LK heuristic are recombined using Iterative Partial Transcription (IPT). We show that IPT can be classified as a partition crossover. Partition crossovers use the features common to the parents to decompose the evaluation function. Recently, a new generalized partition crossover, known as GPX2, was proposed for the TSP. We investigate the use of GPX2 in multi-trial LKH and compare it to multi-trial LKH using IPT. Results of experiments with 11 large instances of the TSP indicate that LKH with GPX2 outperforms LKH with IPT in most of the instances, but not in all of them.