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Socio-cognitive ACO in Multi-criteria Optimization

  • Aleksander ByrskiEmail author
  • Wojciech Turek
  • Wojciech Radwański
  • Marek Kisiel-Dorohinicki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

In this paper a socio-cognitive ACO-type algorithm is proposed for multi-criteria TSP problem optimization. This algorithm is rooted in psychological inspirations and follows other socio-cognitive swarm intelligence methods proposed up to now. This paper presents the idea and shows the applicability of the proposed algorithm based on selected benchmark functions from the scope of well-known TSPLIB library.

Keywords

Multi-criteria optimization Ant-colony algorithm Socio-cognitive computing 

Notes

Acknowledgments

The research presented in this paper was partially supported by the funds of Polish Ministry of Science and Higher Education assigned to AGH University of Science and Technology.

References

  1. 1.
    Barán, B., Schaerer, M.: A multiobjective ant colony system for vehicle routing problem with time windows. In: Applied Informatics, pp. 97–102 (2003)Google Scholar
  2. 2.
    Barán, B., Schaerer, M.: A multiobjective ant colony system for vehicle routing problem with time windows. In: Proceedings of the Twenty First IASTED International Conference on Applied Informatics, pp. 97–102. IASTED (2003)Google Scholar
  3. 3.
    Bugajski, I., Byrski, A., Kisiel-Dorohinicki, M., Lenaerts, T., Samson, D., Indurkhya, B.: Adaptation of population structure in socio-cognitive particle swarm optimization. Proc. Comput. Sci. 101, 177–186 (2016).  https://doi.org/10.1016/j.procs.2016.11.022. http://www.sciencedirect.com/science/article/pii/S1877050916326898. 5th International Young Scientist Conference on Computational Science, YSC 2016, 26-28 October 2016, Krakow, PolandCrossRefGoogle Scholar
  4. 4.
    Bugajski, I., et al.: Enhancing particle swarm optimization with socio-cognitive inspirations. In: Connolly, M. (ed.) International Conference on Computational Science, ICCS 2016, 6–8 June 2016, San Diego, California, USA (2016). Proc. Comput. Sci. 80, pp. 804–813 (2016).  https://doi.org/10.1016/j.procs.2016.05.370CrossRefGoogle Scholar
  5. 5.
    Bukowski, H., Curtain, A., Samson, D.: Can you resist the influence of others? Altercentrism, egocentrism and interpersonal personality traits. In: Proceedings of the Annual Meeting of the Belgian Association for Psychological Sciences (BAPS). Universite catholique de Louvain (2013)Google Scholar
  6. 6.
    Bukowski, H.: What influences perspective taking? A dynamic and multidimensional approach. Ph.D. thesis, Université catholique de Louvain (2014)Google Scholar
  7. 7.
    Bukowski, H., Samson, D.: Can emotions affect level 1 visual perspective taking? Cogn. Neurosci. (in press).  https://doi.org/10.1080/17588928.2015.1043879CrossRefGoogle Scholar
  8. 8.
    Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the ant system. A computational study (1997)Google Scholar
  9. 9.
    Byrski, A.: Socio-cognitive Metaheuristics Computing. AGH University of Science and Technology Press, Krakow (2018)Google Scholar
  10. 10.
    Byrski, A., et al.: Emergence of population structure in socio-cognitively inspired ant colony optimization. Comput. Sci. (AGH) 19(1) (2018).  https://doi.org/10.7494/csci.2018.19.1.2594CrossRefGoogle Scholar
  11. 11.
    Byrski, A., et al.: Socio-cognitively inspired ant colony optimization. J. Comput. Sci. 21, 397–406 (2017).  https://doi.org/10.1016/j.jocs.2016.10.010MathSciNetCrossRefGoogle Scholar
  12. 12.
    Cao, Y., Smucker, B.J., Robinson, T.J.: On using the hypervolume indicator to compare pareto fronts: applications to multi-criteria optimal experimental design. J. Stat. Plan. Infer. 160, 60–74 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Choudhury, S., Blakemore, S.J., Charman, T.: Social cognitive development during adolescence. Soc. Cogn. Affect. Neurosci. 1(3), 165–174 (2006)CrossRefGoogle Scholar
  14. 14.
    Doerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C.: Pareto ant colony optimization: a metaheuristic approach to multiobjective portfolio selection. Ann. Oper. Res. 131(1–4), 79–99 (2004)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 2, pp. 1470–1477. IEEE (1999)Google Scholar
  16. 16.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)CrossRefGoogle Scholar
  17. 17.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, New York (1999)Google Scholar
  18. 18.
    Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Technical report, IRIDIA/98-10, Université Libre de Bruxelles, Belgium (1999)CrossRefGoogle Scholar
  19. 19.
    Feldman, H., Rand, M.E.: Egocentrism-altercentrism in the husband-wife relationship. J. Marriage Family 27(3), 386–391 (1965)CrossRefGoogle Scholar
  20. 20.
    Fizke, E., Barthel, D., Peters, T., Rakoczy, H.: Executive function plays a role in coordinating different perspectives, particularly when one’s own perspective is involved. Cognition 130(3), 315–334 (2014).  https://doi.org/10.1016/j.cognition.2013.11.017CrossRefGoogle Scholar
  21. 21.
    Gambardella, L.M., Taillard, E., Agazzi, G.: A multiple ant colony system for vehicle routing problems with time window. In: New Ideas in Optimization, pp. 63–76. McGraw-Hill (1999)Google Scholar
  22. 22.
    García-Martínez, C., Cordón, O., Herrera, F.: A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria tsp. Eur. J. Oper. Res. 180(1), 116–148 (2007)CrossRefGoogle Scholar
  23. 23.
    Gravel, M., Price, W.L., Gagné, C.: Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. Eur. J. Oper. Res. 143(1), 218–229 (2002)CrossRefGoogle Scholar
  24. 24.
    Johnson, M., Demiris, Y.: Perceptual perspective taking and action recognition. Int. J. Adv. Robot. Syst. 2(4), 301–308 (2005)CrossRefGoogle Scholar
  25. 25.
    Nadel, J.: Some reasons to link imitation and imitation recognition to theory of mind. In: Doric, J., Proust, J. (eds.) Simulation and Knowledge of Action, pp. 119–135. John Benjamins, New York (2002)CrossRefGoogle Scholar
  26. 26.
    Sörensen, K.: Metaheuristics the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015).  https://doi.org/10.1111/itor.12001MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Stützle, T., Hoos, H.H.: Max-min ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)CrossRefGoogle Scholar
  28. 28.
    Vose, M.D.: The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  29. 29.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997).  https://doi.org/10.1109/4235.585893CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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