New neighborhoods and an iterated local search algorithm for the generalized traveling salesman problem

Abstract

For a given graph with a vertex set that is partitioned into clusters, the generalized traveling salesman problem (GTSP) is the problem of finding a cost-minimal cycle that contains exactly one vertex of every cluster. We introduce three new GTSP neighborhoods that allow the simultaneous permutation of the sequence of the clusters and the selection of vertices from each cluster. The three neighborhoods and some known neighborhoods from the literature are combined into an effective iterated local search (ILS) for the GTSP. The ILS performs a straightforward random neighborhood selection within the local search and applies an ordinary record-to-record ILS acceptance criterion. The computational experiments on four symmetric standard GTSP libraries show that, with some purposeful refinements, the ILS can compete with state-of-the-art GTSP algorithms. • Effective iterated local search for generalized traveling salesman problem. • Local search uses several neighborhoods in variable neighborhood descent (VND). • Three new neighborhoods combined with traditional neighborhoods. • Random VND is compared with VND with neighborhood prioritization.