Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search pp 287-299 | Cite as
A Genetic Algorithm for the Clustered Traveling Salesman Problem with a Prespecified Order on the Clusters
Chapter
Abstract
The Clustered Traveling Salesman Problem is an extension of the classical Traveling Salesman Problem, where the set of vertices is partitioned into clusters. The goal is to find the shortest tour such that the clusters are visited in a prespecified order and all vertices within each cluster are visited contiguously. In this paper, a genetic algorithm is proposed to solve this problem. Computational results are reported on a set of Euclidean problems and a comparison is provided with a recent heuristic.
Keywords
Genetic Algorithm Traveling Salesman Problem Travel Salesman Problem Crossover Operator Simple Genetic Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- [1]Baker, J.E. (1985), Adaptive Selection Methods for Genetic Algorithms, in Proceedings of the Int. Conf. on Genetic Algorithms, Pittsburgh, PA, 101–111.Google Scholar
- [2]Baker, J.E. (1987), Reducing Bias and Inefficiency in the Selection Algorithm, in Proceedings of the Second Int. Conf. on Genetic Algorithms, Cambridge, MA, 14–21.Google Scholar
- [3]Chisman, J.A. (1975), The Clustered Traveling Salesman Problem, Computers & Operations Research 2, 115–119.CrossRefGoogle Scholar
- [4]Gendreau, M., A. Hertz and G. Laporte (1992), New Insertion and Postoptimization Procedures for the Traveling Salesman Problem, Operations Research 40, 1086 1094.Google Scholar
- [5]Gendreau, M., G. Laporte and J.Y. Potvin (1994), Heuristics for the Clustered Traveling Salesman Problem, Technical Report CRT-94–54, Centre de recherche sur les transports, Université de Montréal.Google Scholar
- [6]Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Reading: Addison Wesley.Google Scholar
- [7]Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor.Google Scholar
- [8]Jongens, K. and T. Volgenant (1985), The Symmetric Clustered Traveling Salesman Problem, European Journal of Operational Research 19, 68–75.CrossRefGoogle Scholar
- [9]Lin, S. (1965), Computer Solutions of the Traveling Salesman Problem, Bell System Technical Journal 44, 2245–2269.Google Scholar
- [10]Little, J.D.C., K.G. Murty, D.W. Sweeney and C. Karel (1963), An Algorithm for the Traveling Salesman Problem, Operations Research 11, 972–989.CrossRefGoogle Scholar
- [11]Lokin, F.C.J. (1978), Procedures for Travelling Salesman Problems with Additional Constraints, European Journal of Operational Research 3, 135–141.CrossRefGoogle Scholar
- [12]Michalewicz, Z. (1992), Genetic Algorithms + Data Structures = Evolution Programs, Berlin: Springer-Verlag.Google Scholar
- [13]Syswerda, G. (1989), Uniform crossover in Genetic Algorithms, in Proceedings of the Third Int. Conf. on Genetic Algorithms, Fairfax, VA, 2–9.Google Scholar
- [14]Whitley, D. (1989), The Genitor Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best, in Proceedings of the Third Int. Conf. on Genetic Algorithms, Fairfax, VA, 116–121.Google Scholar
- [15]Whitley, D., T. Starkweather and D. Fuquay (1989), Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator, in Proceedings of the Third Int. Conf. on Genetic Algorithms, Fairfax, VA, 133–140.Google Scholar
Copyright information
© Springer Science+Business Media New York 1998