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Agent-Based Approach to Continuous Optimisation

  • Aleksander ByrskiEmail author
  • Marek Kisiel-Dorohinicki
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)

Abstract

In the paper an application of selected agent-based evolutionary computing systems, such as flock-based multi agent system (FLOCK) and evolutionary multi-agent system (EMAS), to the problem of continuous optimisation is presented. Hybridising of agent-based paradigm with evolutionary computation brings a new quality to the meta-heuristic field, easily enhancing individuals with possibilities of perception, interaction with other individuals (agents), adaptation of the search parameters, etc. The experimental examination of selected benchmarks allows to gather the observation regarding the overall efficiency of the systems in comparison to the classical genetic algorithm(as defined by Michalewicz) and memetic versions of all the systems.

Keywords

evolutionary algorithms continuous optimisation multi-agent computing systems memetic computation 

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References

  1. 1.
    Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. IOP Publishing and Oxford University Press (1997)Google Scholar
  2. 2.
    Byrski, A., Dreżewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. The Knowledge Engineering Review (2012)Google Scholar
  3. 3.
    Byrski, A., Kisiel-Dorohinicki, M.: Evolving RBF networks in a multi-agent system. Neural Network World 12(2), 433–440 (2002)Google Scholar
  4. 4.
    Byrski, A., Kisiel-Dorohinicki, M.: Immune-based optimization of predicting neural networks. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 703–710. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Byrski, A., Kisiel-Dorohinicki, M.: Agent-based model and computing environment facilitating the development of distributed computational intelligence systems. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part II. LNCS, vol. 5545, pp. 865–874. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Byrski, A., Schaefer, R.: Stochastic model of evolutionary and immunological multi-agent systems: Mutually exclusive actions. Fundamenta Informaticae 95(2-3), 263–285 (2009)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. Tech. Rep. IlliGAL Report No. 95007, Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (1995)Google Scholar
  8. 8.
    Cetnarowicz, K., Kisiel-Dorohinicki, M., Nawarecki, E.: The application of evolution process in Multi-Agent World (MAW) to the prediction system. In: Tokoro, M. (ed.) Proceedings of the 2nd International Conference on Multi-Agent Systems (ICMAS 1996). AAAI Press (1996)Google Scholar
  9. 9.
    Dawkins, R.: The Selfish Gene: 30th Anniversary edition. Oxford University Press, New York City (2006)Google Scholar
  10. 10.
    Digalakis, J.G., Margaritis, K.G.: An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathemathics 79(4), 403–416 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Hiroyasu, T., Miki, M., Hamasaki, M., Tanimura, Y.: A new model of parallel distributed genetic algorithms for cluster systems: Dual individual DGAs. In: Valero, M., Joe, K., Kitsuregawa, M., Tanaka, H. (eds.) ISHPC 2000. LNCS, vol. 1940, pp. 374–383. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Kisiel-Dorohinicki, M.: Agent-oriented model of simulated evolution. In: Grosky, W.I., Plášil, F. (eds.) SOFSEM 2002. LNCS, vol. 2540, pp. 253–261. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Kisiel-Dorohinicki, M.: Flock-based architecture for distributed evolutionary algorithms. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 841–846. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Kisiel-Dorohinicki, M., Dobrowolski, G., Nawarecki, E.: Agent populations as computational intelligence. In: Rutkowski, L., Kacprzyk, J. (eds.) Proceedings of 6th International Conference on Neural Networks and Soft Computing (ICNNSC 2002). Advances in Soft Computing, vol. 19, pp. 608–613. Physica-Verlag (2003)Google Scholar
  15. 15.
    Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: Model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation 9(5), 474–488 (2005)CrossRefGoogle Scholar
  16. 16.
    Michalewicz, Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs, 2nd edn. Springer (1994)Google Scholar
  17. 17.
    Moscato, P.: Memetic algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill (1999)Google Scholar
  18. 18.
    Moscato, P., Cotta, C.: A modern introduction to memetic algorithms. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 141–183. Springer, US (2010)CrossRefGoogle Scholar
  19. 19.
    Ong, Y.S., Lim, M.H., Che, X.: Memetic computation – past, present & future. IEEE Computational Intelligence Magazine 5(2), 24–31 (2010)CrossRefGoogle Scholar
  20. 20.
    Schaefer, R., Byrski, A., Smołka, M.: Stochastic model of evolutionary and immunological multi-agent systems: Parallel execution of local actions. Fundamenta Informaticae 95(2-3), 325–348 (2009)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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