Abstract
A new heterogeneous algorithm of Discrete Particle Swarm Optimization has been proposed in this paper to solve the Dynamic Traveling Salesman problem. The test environment is random and variable in time, what requires a rapid adaptation of the algorithm to changes. An inappropriate selection of the algorithm parameters leads to stagnation and quality deterioration of obtained results. The modification, proposed in this paper, enables to reduce the number of the algorithm parameters, regarding the swarm size, the number of iterations and the size of neighborhood. A higher diversity of particles vs. the homogeneous version positively influences the quality of obtained results, what was demonstrated in various experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Branke, J.: Evolutionary approaches to dynamic environments. In: GECCO Workshop on Evolutionary Algorithms for Dynamics Optimization Problems (2001)
Li, W.: A parallel multi-start search algorithm for dynamic traveling salesman problem. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 65–75. Springer, Heidelberg (2011)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
de Oca, M.A.M., Peña, J., Stützle, T., Pinciroli, C., Dorigo, M.: Heterogeneous particle swarm optimizers. In: Haddow, P., et al. (eds.) IEEE Congress on Evolutionary Computation, CEC 2009, pp. 698–705. IEEE Press, Piscatway (2009)
Hansell, M.: Built by Animals. Oxford University Press, New York (2007)
Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO inspired by ants. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 188–195. Springer, Heidelberg (2012)
Boryczka, U., Strąk, Ł.: Efficient DPSO neighbourhood for dynamic traveling salesman problem. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds.) ICCCI 2013. LNCS, vol. 8083, pp. 721–730. Springer, Heidelberg (2013)
Zhong, W.L., Zhang, J., Chen, W.N.: A novel set-based particle swarm optimization method for discrete optimization problems. In: Evolutionary Computation, CEC 2007, vol. 14, pp. 3283–3287. IEEE (1997)
Helsgaun, K.: An effective implementation of k-opt moves for the Lin-Kernighan TSP heuristic. Technical report, Roskilde University (2006)
Stützle, T., Hoos, H.H.: Max-min ant system. Future Generation Computer Systems 16(8), 889–914 (2000)
Helsgaun, K.: An effective implementation of the lin-kernighan traveling salesman heuristic. European Journal of Operational Research 126, 106–130 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Boryczka, U., Strąk, Ł. (2015). Heterogeneous DPSO Algorithm for DTSP. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-24306-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24305-4
Online ISBN: 978-3-319-24306-1
eBook Packages: Computer ScienceComputer Science (R0)