Advertisement

Hybrid Swarm and Agent-Based Evolutionary Optimization

  • Leszek Placzkiewicz
  • Marcin Sendera
  • Adam Szlachta
  • Mateusz Paciorek
  • Aleksander Byrski
  • Marek Kisiel-Dorohinicki
  • Mateusz Godzik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

In this paper a novel hybridization of agent-based evolutionary system (EMAS, a metaheuristic putting together agency and evolutionary paradigms) is presented. This method assumes utilization of particle swarm optimization (PSO) for upgrading certain agents used in the EMAS population, based on agent-related condition. This may be perceived as a method similar to local-search already used in EMAS (and many memetic algorithms). The obtained and presented in the end of the paper results show the applicability of this hybrid based on a selection of a number of 500 dimensional benchmark functions, when compared to non-hybrid, classic EMAS version.

Notes

Acknowlegment

The research presented in this paper was partially supported by the Grant of the Dean of Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, for Ph.D. Students.

References

  1. 1.
    Abd-El-Wahed, W.F., Mousa, A.A., El-Shorbagy, M.A.: Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J. Comput. Appl. Math. 235(5), 1446–1453 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Borna, K., Khezri, R.: A combination of genetic algorithm and particle swarm optimization method for solving traveling salesman problem. Cogent Math. 2(1) (2015)Google Scholar
  3. 3.
    Byrski, A., Schaefer, R., Smołka, M., Cotta, C.: Asymptotic guarantee of success for multi-agent memetic systems. Bull. Pol. Acad. Sci.-Tech. Sci. 61(1), 257–278 (2013)Google Scholar
  4. 4.
    Byrski, A., Debski, R., Kisiel-Dorohinicki, M.: Agent-based computing in an augmented cloud environment. Comput. Syst. Sci. Eng. 27(1), 7–18 (2012)Google Scholar
  5. 5.
    Byrski, A., Dreżewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. Knowl. Eng. Rev. 30(2), 171–186 (2015)CrossRefGoogle Scholar
  6. 6.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois (1995)Google Scholar
  7. 7.
    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), pp. 26–32. AAAI Press (1996)Google Scholar
  8. 8.
    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
  9. 9.
    Gupta, M., Yadav, R.: New improved fractional order differentiator models based on optimized digital differentiators. Sci. World J. 2014, Article ID 741395 (2014)Google Scholar
  10. 10.
    Kao, Y.-T., Zahara, E.: A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Comput. 8(2), 849–857 (2008)CrossRefGoogle Scholar
  11. 11.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995Google 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).  https://doi.org/10.1007/3-540-36137-5_19CrossRefGoogle Scholar
  13. 13.
    Korczynski, W., Byrski, A., Kisiel-Dorohinicki, M.: Buffered local search for efficient memetic agent-based continuous optimization. J. Comput. Sci. 20(Suppl. C), 112–117 (2017)Google Scholar
  14. 14.
    Kuo, R.J., Han, Y.S.: A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem - a case study on supply chain model. Appl. Math. Model. 35(8), 3905–3917 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mousavi, M., Yap, H.J., Musa, S.N., Tahriri, F., Md Dawal, S.Z.: Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PLOS ONE 12(3), 1–24 (2017)CrossRefGoogle Scholar
  16. 16.
    Nazir, M., Majid-Mirza, A., Ali-Khan, S.: PSO-GA based optimized feature selection using facial and clothing information for gender classification. J. Appl. Res. Technol. 12(1), 145–152 (2014)CrossRefGoogle Scholar
  17. 17.
    Singh, A., Garg, N., Saini, T.: A hybrid approach of genetic algorithm and particle swarm technique to software test case generation. Int. J. Innov. Eng. Technol. 3, 208–214 (2014)Google Scholar
  18. 18.
    Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Li, W.T., Xu, L., Shi, X.W.: A hybrid of genetic algorithm and particle swarm optimization for antenna design. In: Progress in Electromagnetics Research Symposium, vol. 2 (2008)Google Scholar
  20. 20.
    Thangaraj, R., Pant, M., Abraham, A., Bouvry, P.: Particle swarm optimization: hybridization perspectives and experimental illustrations. Appl. Math. Comput. 217(12), 5208–5226 (2011)zbMATHGoogle Scholar
  21. 21.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 67(1), 67–82 (1997)CrossRefGoogle Scholar
  22. 22.
    Xu, S.-H., Liu, J.-P., Zhang, F.-H., Wang, L., Sun, L.-J.: A combination of genetic algorithm and particle swarm optimization for vehicle routing problem with time windows. Sensors 15(9), 21033–21053 (2015)CrossRefGoogle Scholar
  23. 23.
    Ykhlef, M., Alqifari, R.: A new hybrid algorithm to solve winner determination problem in multiunit double internet auction. 2015, 1–10 (2015)Google Scholar
  24. 24.
    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 International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Leszek Placzkiewicz
    • 1
  • Marcin Sendera
    • 1
  • Adam Szlachta
    • 1
  • Mateusz Paciorek
    • 1
  • Aleksander Byrski
    • 1
  • Marek Kisiel-Dorohinicki
    • 1
  • Mateusz Godzik
    • 1
  1. 1.Department of Computer Science, Faculty of Computer Science, Electronics and TelecommunicationsAGH University of Science and TechnologyKrakowPoland

Personalised recommendations