GLNS: An Effective Large Neighborhood Search Heuristic for the Generalized Traveling...

A new solver for the Generalized Traveling Salesman Problem (GTSP). Solver utilizes adaptive large neighborhood search. Proposes novel insertion mechanism for GTSP tour construction.Extensive benchmarking shows substantial improvements over state-of-the-art. Solver performance stands out on highly constrained non-metric instances. This paper presents a new solver for the exactly one-in-a-set Generalized Traveling Salesman Problem (GTSP). In the GTSP, we are given as input a complete directed graph with edge weights, along with a partition of the vertices into disjoint sets. The objective is to find a cycle (or tour) in the graph that visits each set exactly once and has minimum length. In this paper we present an effective algorithm for the GTSP based on adaptive large neighborhood search. The algorithm operates by repeatedly removing from, and inserting vertices in, the tour. We propose a general insertion mechanism that contains as special cases the well-known nearest, farthest and random insertion mechanisms. We provide extensive benchmarking results for our solver in comparison to the state-of-the-art on a wide range of existing and new problem libraries. We show that on the GTSP-LIB library, the proposed algorithm is competitive with the best known algorithms. On several other libraries, we show that given the same amount of time, the proposed solver finds higher quality solutions than existing approaches, particularly on harder instances that are non-metric and/or whose sets are not clustered.

Английский

Computers & Operations Research. – 2017. – Vol. 87. – P. 1–19.