The Competing Travelling Salespersons Problem Under Multi-criteria

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9921
• 期：1
• 页码：463-472
• 全文大小：1,145 KB
• 参考文献：1.Ehrgott, M., Gandibleux, X.: An annotated bibliography of multiobjective combinatorial optimization, pp. 1–60 (2000)
  2.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)CrossRef
  3.Fekete, S.P., Fleischer, R., Fraenkel, A., Schmitt, M.: Traveling salesmen in the presence of competition. Theor. Comput. Sci. 313(3), 377–392 (2004)MathSciNet CrossRef MATH
  4.Kendall, G., Li, J.: Competitive travelling salesmen problem: a hyper-heuristic approach. J. Oper. Res. Soc. 64, 208–216 (2012)CrossRef
  5.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
  6.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)
  7.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)
  8.Zeleny, M.: Games with multiple payoffs. Int. J. Game Theory 4(4), 179–191 (1975)MathSciNet CrossRef MATH
  9.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)CrossRef
  10.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)
  11.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)CrossRef
  12.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
  
• 作者单位：Erella Matalon-Eisenstadt (19) (20)
  Amiram Moshaiov (19)
  Gideon Avigad (21)
  
  19. School of Mechanical Engineering, Tel Aviv University, Tel Aviv, Israel
  20. Department of Mechanical Engineering, ORT Braude College of Engineering, Karmiel, Israel
  21. Robotics and Automation Department, Vineland Research and Innovation Centre, Lincoln, ON, Canada
  
• 丛书名：Parallel Problem Solving from Nature ¨C PPSN XIV
• ISBN：978-3-319-45823-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9921

文摘

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.