The generalized traveling salesman problem solved with ant algorithms

The generalized traveling salesman problem solved with ant algorithms A well known $$\mathcal{NP}$$ NP -hard problem called the generalized traveling salesman problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System (ACS) algorithm called reinforcing Ant Colony System which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Complex Adaptive Systems Modeling Springer Journals

The generalized traveling salesman problem solved with ant algorithms

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by The Author(s)
Subject
Mathematics; Complex Systems; Operations Research/Decision Theory; Simulation and Modeling; Operations Research, Management Science
eISSN
2194-3206
D.O.I.
10.1186/s40294-017-0048-9
Publisher site
See Article on Publisher Site

Abstract

A well known $$\mathcal{NP}$$ NP -hard problem called the generalized traveling salesman problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System (ACS) algorithm called reinforcing Ant Colony System which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time.

Journal

Complex Adaptive Systems ModelingSpringer Journals

Published: Aug 7, 2017

References

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