Abstract
The paper concerns the introduced and defined problem which was called the Provider. This problem coming from practice and can be treated as a modified version of Travelling Salesman Problem. For solving the problem an algorithm (called ACO) based on ant colony optimization ideas has been created. The properties of the algorithm were tested using the designed and implemented experimentation system. The effectiveness of the algorithm was evaluated and compared to reference results given by another implemented Random Optimization algorithm (called RO) on the basis of simulation experiments. The reported investigations have shown that the ACO algorithm seems to be very effective for solving the considered problem. Moreover, the ACO algorithm can be recommended for solving other transportation problems.
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
Zuhori, S.T.: Traveling Salesman Problem. Lambert Academic Publishing (2012) ISBN:3846583057
Applegate, D.L., Bixby, R.B., Chvatal, V., Cook, W.J.: The travelling salesman problem: A computational study. Princeton Series in Applied Mathematics. Princeton University Press (2007)
Wong, K.-C., Wu, C.-H., Mok, R.K.P., Peng, C., Zhang, Z.: Evolutionary multimodal optimization using the principle of locality. Information Sciences 194(1), 138–170 (2012)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University (1999)
Yang, X.S., Cui, Z.H., Xiao, R.B., Gandomi, A.H., Karamanoglu, M.: Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Elsevier (2013)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)
Fister, I., Fister Jr., I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation (2013), http://dx.doi.org/10.1016/j.swevo.2013.06.001
Lizárraga, E., Castillo, O., Soria, J.: A method to solve the traveling salesman problem using ant colony optimization variants with ant set partitioning. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems. SCI, vol. 451, pp. 237–246. Springer, Heidelberg (2013)
Cormen, T.C., Leiserson, C., Rivest, R.L.: Introduction to algorithms. McGraw Hill (2001)
Kubacki, J., Koszalka, L., Pozniak-Koszalka, I., Kasprzak, A.: Comparison of heuristic algorithms to solving mesh network path finding problem. In: Proceedings to 4th International Conference on Frontier of Computer Science and Technology, Shanghai. IEEE (2009)
Regula, P., Pozniak-Koszalka, I., Koszalka, L., Kasprzak, A.: Evolutionary algorithms for base stations placement in mobile networks. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS (LNAI), vol. 6592, pp. 1–10. Springer, Heidelberg (2011)
Kakol, A., Pozniak-Koszalka, I., Koszalka, L., Kasprzak, A., Burnham, K.J.: An experimentation system for testing bee behavior based algorithm to solving a transportation problem. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS (LNAI), vol. 6592, pp. 11–20. Springer, Heidelberg (2011)
Neyoy, H., Castillo, O., Soria, J.: Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems. SCI, vol. 451, pp. 259–271. Springer, Heidelberg (2013)
Castillo, O.: ACO-tuning of a fuzzy controller for the ball and beam problem. In: Castillo, O. (ed.) Type-2 Fuzzy Logic in Intelligent Control Applications. STUDFUZZ, vol. 272, pp. 151–159. Springer, Heidelberg (2012)
Basu, S.: Tabu search implementation on traveling salesman problem and its variations: a literature survey. American Journal of Operations Research 2(2), 163–173 (2012)
Bhattacharyya, M., Bandyopadhyay, A.K.: Comparative study of some solution methods for traveling salesman problem using genetic algorithms. Cybernetics and Systems 40(1), 1–24 (2008)
Ouaarab, A., Ahiod, B., Yang, X.S.: Discrete cuckoo search algorithm for the traveling salesman problem. Neural Computing and Applications (April 2013), http://link.springer.com/article/10.1007%00521-013-1402-2
Ohia, D., Koszalka, L., Kasprzak, A.: Evolutionary algorithm for solving congestion problem in computer network. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009, Part I. LNCS (LNAI), vol. 5711, pp. 112–121. Springer, Heidelberg (2009)
Martinez, A.C., Castillo, O., Montiel, O.: Comparison between ant colony and genetic algorithms for fuzzy system optimization. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Soft Computing for Hybrid Intelligent Systems. SCI, vol. 154, pp. 71–86. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Baranowski, K., Koszałka, L., Poźniak-Koszałka, I., Kasprzak, A. (2014). Ant Colony Optimization Algorithm for Solving the Provider - Modified Traveling Salesman Problem. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-05476-6_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05475-9
Online ISBN: 978-3-319-05476-6
eBook Packages: Computer ScienceComputer Science (R0)