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Differential Evolution in Agent-Based Computing

  • Mateusz GodzikEmail author
  • Bartlomiej Grochal
  • Jakub Piekarz
  • Mikolaj Sieniawski
  • Aleksander ByrskiEmail author
  • Marek Kisiel-Dorohinicki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

Evolutionary multi-agent systems (EMAS) turned out to be quite efficient technique for solving complex problems, both benchmark ones (as well-known multi-dimensional functions, e.g. Rastrigin, Schwefel etc) and more practical ones (like Optimal Golomb Ruler or Low Autocorrelation Binary Sequence). However the already classic design of the EMAS (these metaheuristics have been developed for over 15 years) has still many places for improvement. Hybridization is one of such means, and it turns out that incorporating Differential Evolution mechanisms into EMAS (altering the reproduction strategy by making it more social-aware) improves the accuracy of the search. This paper deals with discussion of selected means for hybridization of EMAS with DE, and provides an insight into the efficacy of the novel algorithm compared with classic techniques based on multidimensional benchmark problems.

Keywords

Metaheuristics Agent-based computing Differential evolution Hybrid algorithms 

Notes

Acknowledgment

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

References

  1. 1.
    Byrski, A., Schaefer, R., Smołka, M.: Asymptotic guarantee of success for multi-agent memetic systems. Bull. Pol. Acad. Sci. Tech. Sci. 61(1), 257–278 (2013)Google Scholar
  2. 2.
    Byrski, A., Debski, R., Kisiel-Dorohinicki, M.: Agent-based computing in an augmented cloud environment. Comput. Syst. Sci. Eng. 27(1) (2012)Google Scholar
  3. 3.
    Byrski, A., Drezewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. Knowl. Eng. Rev. 30(2), 171–186 (2015).  https://doi.org/10.1017/S0269888914000289CrossRefGoogle Scholar
  4. 4.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois (1995)Google Scholar
  5. 5.
    Caponio, A., Neri, F., Tirronen, V.: Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput. 13(8), 811–831 (2009).  https://doi.org/10.1007/s00500-008-0357-1CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Das, S., Konar, A., Chakraborty, U.K.: Improving particle swarm optimization with differentially perturbed velocity. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 177–184. ACM (2005)Google Scholar
  8. 8.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011).  https://doi.org/10.1109/TEVC.2010.2059031CrossRefGoogle Scholar
  9. 9.
    Digalakis, J., Margaritis, K.: An experimental study of benchmarking functions for evolutionary algorithms. Int. J. Comput. Math. 79(4), 403–416 (2002). citeseer.ist.psu.edu/digalakis02experimental.htmlMathSciNetCrossRefGoogle Scholar
  10. 10.
    Franklin, S., Graesser, A.: Is It an agent, or just a program? A taxonomy for autonomous agents. In: Müller, J.P., Wooldridge, M.J., Jennings, N.R. (eds.) ATAL 1996. LNCS, vol. 1193, pp. 21–35. Springer, Heidelberg (1997).  https://doi.org/10.1007/BFb0013570CrossRefGoogle Scholar
  11. 11.
    Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. In: Grmela, A., Mastorakis, N. (eds.) Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298. WSEAS Press (2002)Google Scholar
  12. 12.
    He, X., Han, L.: A novel binary differential evolution algorithm based on artificial immune system. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2267–2272. IEEE (2007)Google Scholar
  13. 13.
    Hendtlass, T.: A combined swarm differential evolution algorithm for optimization problems. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS (LNAI), vol. 2070, pp. 11–18. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45517-5_2CrossRefGoogle Scholar
  14. 14.
    Jitkongchuen, D.: A hybrid differential evolution with grey wolf optimizer for continuous global optimization. In: Proceedings of the 7th International Conference on Information Technology and Electrical Engineering, pp. 51–54. IEEE (2015)Google Scholar
  15. 15.
    Kannan, S., Slochanal, S.M.R., Subbaraj, P., Padhy, N.P.: Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr. Power Syst. Res. 70(3), 203–210 (2004).  https://doi.org/10.1016/j.epsr.2003.12.009CrossRefGoogle Scholar
  16. 16.
    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).  https://doi.org/10.1007/3-540-36137-5_19CrossRefGoogle Scholar
  17. 17.
    Korczynski, W., Byrski, A., Kisiel-Dorohinicki, M.: Buffered local search for efficient memetic agent-based continuous optimization. J. Comput. Sci. 20(Supplement C), 112–117 (2017).  https://doi.org/10.1016/j.jocs.2017.02.001. http://www.sciencedirect.com/science/article/pii/S1877750317301345CrossRefzbMATHGoogle Scholar
  18. 18.
    Liao, T.W.: Two hybrid differential evolution algorithms for engineering design optimization. Appl. Soft Comput. 10(4), 1188–1199 (2010).  https://doi.org/10.1016/j.asoc.2010.05.007CrossRefGoogle Scholar
  19. 19.
    Liu, K., Du, X., Kang, L.: Differential evolution algorithm based on simulated annealing. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 120–126. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74581-5_13CrossRefGoogle Scholar
  20. 20.
    Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008).  https://doi.org/10.1109/TEVC.2007.895272CrossRefGoogle Scholar
  21. 21.
    Rahmat, N.A., Musirin, I.: Differential Evolution Ant Colony Optimization (DEACO) technique in solving economic load dispatch problem. In: Proceedings of the IEEE International Power Engineering and Optimization Conference, pp. 263–268. IEEE (2012)Google Scholar
  22. 22.
    Sörensen, K.: Metaheuristics–the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015).  https://doi.org/10.1111/itor.12001MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, ICSI, USA, March 1995Google Scholar
  24. 24.
    Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 67(1) (1997)Google Scholar
  25. 25.
    Yang, Z., Yao, X., He, J.: Making a difference to differential evolution. In: Siarry, P., Michalewicz, Z. (eds.) Advances in Metaheuristics for Hard Optimization, pp. 397–414. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-72960-0_19CrossRefGoogle Scholar
  26. 26.
    Zhang, W.J., Xie, X.F.: DEPSO: hybrid particle swarm with differential evolution operator. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3816–3821. IEEE (2003)Google Scholar
  27. 27.
    Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(2), 1128–1141 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mateusz Godzik
    • 1
    Email author
  • Bartlomiej Grochal
    • 1
  • Jakub Piekarz
    • 1
  • Mikolaj Sieniawski
    • 1
  • Aleksander Byrski
    • 1
    Email author
  • Marek Kisiel-Dorohinicki
    • 1
  1. 1.Department of Computer Science, Faculty of Computer Science, Electronics and TelecommunicationsAGH University of Science and TechnologyKrakówPoland

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