Abstract
This paper introduces a novel type of a problem in which two travelling salespersons are competing, where each of them has two conflicting objectives. This problem is categorized as a Multi-Objective Game (MOG). It is solved by a non-utility approach, which has recently been introduced. According to this method all rationalizable strategies are initially found, to support posteriori decision on a strategy. An evolutionary algorithm is proposed to search for the set of rationalizable strategies. The applicability of the suggested algorithm is successfully demonstrated on the presented new type of problem.
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
Similar content being viewed by others
References
Ehrgott, M., Gandibleux, X.: An annotated bibliography of multiobjective combinatorial optimization, pp. 1–60 (2000)
Manthey, B., Ram, L.S.: Approximation algorithms for multi-criteria traveling salesman problems. In: Erlebach, T., Kaklamanis, C. (eds.) WAOA 2006. LNCS, vol. 4368, pp. 302–315. Springer, Heidelberg (2007)
Fekete, S.P., Fleischer, R., Fraenkel, A., Schmitt, M.: Traveling salesmen in the presence of competition. Theor. Comput. Sci. 313(3), 377–392 (2004)
Kendall, G., Li, J.: Competitive travelling salesmen problem: a hyper-heuristic approach. J. Oper. Res. Soc. 64, 208–216 (2012)
Eisenstadt, E., Moshaiov, A., Avigad, G., Branke, J.: Rationalizable strategies in multi-objective games under undecided objective preferences (2016). www.eng.tau.ac.il/~moshaiov/MOGJOTA.pdf
Avigad, G., Eisenstadt, E., Weiss-Cohen, M.: Optimal strategies for multi objective games and their search by evolutionary multi objective optimization. In: 2011 IEEE Conference on Computational Intelligence and Games (CIG), pp. 166–173 (2011)
Eisenstadt, E., Moshaiov, A., Avigad, G.: Co-evolution of strategies for multi-objective games under postponed objective preferences. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG), pp. 461–468 (2015)
Zeleny, M.: Games with multiple payoffs. Int. J. Game Theory 4(4), 179–191 (1975)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Avigad, G., Branke, J.: Embedded evolutionary multi-objective optimization for worst case robustness. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 617–624 (2008)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)
Eisenstadt, E., Moshaiov, A., Avigad, G.: Testing and comparing multi-objective evolutionary algorithms for multi-payoff games. https://www.eng.tau.ac.il/~moshaiov/TEC.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Matalon-Eisenstadt, E., Moshaiov, A., Avigad, G. (2016). The Competing Travelling Salespersons Problem Under Multi-criteria. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-45823-6_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45822-9
Online ISBN: 978-3-319-45823-6
eBook Packages: Computer ScienceComputer Science (R0)