A Genetic Algorithm for the Clustered Traveling Salesman Problem with a Prespecified Order on the Clusters
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.
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- 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
- 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
- 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
- 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
- Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Reading: Addison Wesley.Google Scholar
- Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor.Google Scholar
- Lin, S. (1965), Computer Solutions of the Traveling Salesman Problem, Bell System Technical Journal 44, 2245–2269.Google Scholar
- Michalewicz, Z. (1992), Genetic Algorithms + Data Structures = Evolution Programs, Berlin: Springer-Verlag.Google Scholar
- Syswerda, G. (1989), Uniform crossover in Genetic Algorithms, in Proceedings of the Third Int. Conf. on Genetic Algorithms, Fairfax, VA, 2–9.Google Scholar
- 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
- 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