An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem
This work addresses a combinatorial optimization problem relevant to logistics and routing, but it is incremental as it modifies an existing method for a known bottleneck.
The paper tackled the Generalized Traveling Salesman Problem (GTSP) by developing a hybrid Ant Colony System algorithm enhanced with a GTSP-specific local search, resulting in dramatic performance improvements that make it one of the most successful metaheuristics for GTSP.
The Generalized Traveling Salesman Problem (GTSP) is an extension of the well-known Traveling Salesman Problem (TSP), where the node set is partitioned into clusters, and the objective is to find the shortest cycle visiting each cluster exactly once. In this paper, we present a new hybrid Ant Colony System (ACS) algorithm for the symmetric GTSP. The proposed algorithm is a modification of a simple ACS for the TSP improved by an efficient GTSP-specific local search procedure. Our extensive computational experiments show that the use of the local search procedure dramatically improves the performance of the ACS algorithm, making it one of the most successful GTSP metaheuristics to date.