Abstract
This paper presents a variation of the Euclidean Traveling Salesman Problem (TSP), the Multiple Traveling Salesman Problem (MTSP), and compares a variety of evolutionary computation algorithms and paradigms for solving it. Techniques implemented, analyzed, and discussed herein with regard to MTSP include use of a neighborhood attractor schema (a variation on k-means clustering), the “shrink-wrap” algorithm for local neighborhood optimization, particle swarm optimization, Monte-Carlo optimization, and a range of genetic algorithms and evolutionary strategies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bellman, R.E., "Dynamic programming treatment of the traveling salesman problem", Journal of the ACM, 9:61–63 (1962)
Bellmore, M. and G.L. Nemhauser, "The traveling salesman problem: a survey", Operations Res. 16, 538–558 (1968)
Karp, R.M., "Probabilistic analysis of partitioning algorithms for the traveling-salesman problem in the plane", Math. of Operations Research, 2:209–224 (1977)
Papadamitriou, C.H., "The Euclidean traveling salesman problem is NP-complete", Theoretical Computer Science, 4:237–244 (1977)
Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G and D.B. Shmoys. The traveling salesman problem. Wiley, New York (1985)
Arora, S., "Nearly linear time approximation schemes for Euclidean TSP and other geometric problems", In 38th Annual Symposium on Foundations of Computer Science, pages 554–563, Miami Beach, Florida, 20–22 October (1997)
Goldberg, D.E., "Messy genetic algorithms: Motivation, Analysis, and First results", Complex Systems, Vol. 3, pp. 493–530 (1989)
Jog, P., Suh, J.Y., and D. Van Gucht, "The Effects of Population Size, Heuristic Crossover and Local Improvement on a Genetic Algorithm for the Traveling Salesman Problem", Proc. of the 3rd Intl. Conference on Genetic Algorithms, pp. 110–115, Morgan Kaufmann, (1989)
Julstrom, B.A., "Insertion Decoding Algorithms and Initial Tours in a Weight-Coded GA for TSP", Genetic Programming 1998: Proc. of the Third Annual Conference, pp. 528–534, Morgan Kaufmann, 22–25 (1998)
Hansen, P., and N. Mladenoviæ, “J-Means: A new local search heuristic for minimum sum-of-squares clustering”, Pattern Recognition, Vol. 34 (2) 405–413, 2001.
De Jong, K., Course on Evolutionary Computation, George Mason University, (1998)
Kennedy, J. and R.C. Eberhart, "Particle Swarm Optimization", Proc. IEEE Intl. Conf. on Neural Networks, pp. IV:1942–1948 (1995)
Eberhart, R.C., and Y. Shi, "Evolving Artificial Neural Networks", Proc. Intl. Conf. on Neural Networks and the Brain-Beijing (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sofge, D., Schultz, A., De Jong, K. (2002). Evolutionary Computational Approaches to Solving the Multiple Traveling Salesman Problem Using a Neighborhood Attractor Schema. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_16
Download citation
DOI: https://doi.org/10.1007/3-540-46004-7_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43432-0
Online ISBN: 978-3-540-46004-6
eBook Packages: Springer Book Archive