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Autonomous Hybridization of Agent-Based Computing

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Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12496))

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Abstract

Using agent-based systems for computing purposes, where agent becomes not only driver for realizing computing task, but a part of the computing itself is an interesting paradigm allowing for easy yet robust design of metaheuristics, making possible easy parallelization and developing new efficient computing methods. Such methods as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) or Evolutionary Multi Agent-System (EMAS) are examples of such algorithms. In the paper novel approach to hybridization of such computing systems is presented. A number of agents doing their computing task can agree to run other algorithm (similarly to high level hybrid proposed by Talbi). The paper focuses on presenting the background and the idea of such algorithm along with firm experimental results.

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Notes

  1. 1.

    jMetal [10] is an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving optimization problems. http://jmetal.github.io/jMetal/.

References

  1. Borna, K., Khezri, R.: A combination of genetic algorithm and particle swarm optimization method for solving traveling salesman problem. Cogent Math. 2(1) (2015). http://doi.org/10.1080/23311835.2015.1048581

  2. 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 

  3. 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 

  4. Byrski, A., Dreżewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. Knowl. Eng. Rev. 30(2), 171–186 (2015). https://doi.org/10.1017/S0269888914000289

    Article  Google Scholar 

  5. Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois (1995)

    Google Scholar 

  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. 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. Chen, Z., Wang, R.: GA and ACO-based hybrid approach for continuous optimization. In: 2015 International Conference on Modeling, Simulation and Applied Mathematics. Atlantis Press (2015). https://doi.org/10.2991/msam-15.2015.81

  9. Ciepiela, E., Kocot, J., Siwik, L., Dreżewski, R.: Hierarchical approach to evolutionary multi-objective optimization. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008. LNCS, vol. 5103, pp. 740–749. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69389-5_82

    Chapter  Google Scholar 

  10. Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011). http://www.sciencedirect.com/science/article/pii/S0965997811001219. https://doi.org/10.1016/j.advengsoft.2011.05.014

  11. Figueiredo, E.M., Ludermir, T.B., Bastos-Filho, C.J.: Many objective particle swarm optimization. Inf. Sci. 374, 115–134 (2016)

    Article  Google Scholar 

  12. 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/BFb0013570. http://dl.acm.org/citation.cfm?id=648203.749270

    Chapter  Google Scholar 

  13. Godzik, M., Grochal, B., Piekarz, J., Sieniawski, M., Byrski, A., Kisiel-Dorohinicki, M.: Differential evolution in agent-based computing. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 228–241. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14802-7_20

    Chapter  Google Scholar 

  14. Kao, Y.T., Zahara, E.: A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Comput. 8(2), 849–857 (2008). http://dx.doi.org/10.1016/j.asoc.2007.07.002

  15. 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_19

    Chapter  Google Scholar 

  16. Korczynski, W., Byrski, A., Kisiel-Dorohinicki, M.: Buffered local search for efficient memetic agent-based continuous optimization. J. Comput. Sci. 20, 112–117 (2017). https://doi.org/10.1016/j.jocs.2017.02.001

  17. Lazarz, R., Idzik, M., Gadek, K., Gajda-Zagorska, E.: Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization. J. Comput. Sci. 17, 249–260 (2016)

    Article  MathSciNet  Google Scholar 

  18. Lazarz, R., Idzik, M., Gadek, K., Gajda-Zagórska, E.: Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization. J. Comput. Science 17, 249–260 (2016). https://doi.org/10.1016/j.jocs.2016.03.004

  19. López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L.P., Birattari, M., Stützle, T.: The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016). https://doi.org/10.1016/j.orp.2016.09.002. http://www.sciencedirect.com/science/article/pii/S2214716015300270

  20. Nebro, A.J., Durillo, J.J., Garcia-Nieto, J., Coello, C.C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE symposium on Computational Intelligence in Multi-Criteria Decision-Making, 2009. MCDM 2009, pp. 66–73. IEEE (2009)

    Google Scholar 

  21. Placzkiewicz, L., et al.: Hybrid swarm and agent-based evolutionary optimization. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10861, pp. 89–102. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_7

    Chapter  Google Scholar 

  22. Podsiadło, K., Łoś, M., Siwik, L., Woźniak, M.: An algorithm for tensor product approximation of three-dimensional material data for implicit dynamics simulations. In: Shi, Y., et al. (eds.) Computational Science - ICCS 2018, pp. 156–168. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_12

    Chapter  Google Scholar 

  23. Rajappa, G.P.: Solving combinatorial optimization problems using genetic algorithms and ant colony optimization. Ph.D. thesis, University of Tennessee (2012). https://trace.tennessee.edu/utk_graddiss/1478

  24. Schaefer, R., Kolodziej, J.: Genetic search reinforced by the population hierarchy. Found. Genet. Algorithms 7, 383–401 (2002)

    Google Scholar 

  25. Sierra, M., Coello, C.: Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance. In: Evolutionary Multi-Criterion Optimization, pp. 505–519 (2005)

    Google Scholar 

  26. Siwik, L., Los, M., Kisiel-Dorohinicki, M., Byrski, A.: Hybridization of isogeometric finite element method and evolutionary multi-agent system as a tool-set for multiobjective optimization of liquid fossil fuel reserves exploitation with minimizing groundwater contamination. Procedia Comput. Sci. 80, 792–803 (2016). https://doi.org/10.1016/j.procs.2016.05.369. http://www.sciencedirect.com/science/article/pii/S1877050916308444. International Conference on Computational Science 2016, ICCS 2016, 6–8 June 2016, San Diego, California, USA

  27. Talbi, E.G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8, 541–564 (2002)

    Article  Google Scholar 

  28. Thangaraj, R., Pant, M., Abraham, A., Bouvry, P.: Particle swarm optimization: Hybridization perspectives and experimental illustrations. Appl. Math. Comput. 217(12), 5208–5226 (2011). https://doi.org/10.1016/j.amc.2010.12.053. http://www.sciencedirect.com/science/article/pii/S0096300310012555

  29. Vose, M.: The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge, MA, USA (1998)

    MATH  Google Scholar 

  30. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 67(1), 67–82 (1997)

    Article  Google Scholar 

  31. 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). https://doi.org/10.3390/s150921033. http://www.mdpi.com/1424-8220/15/9/21033

  32. Zhang, X., Duan, H., Jin, J.: DEACO: hybrid ant colony optimization with differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, 1–6 June 2008, Hong Kong, China, pp. 921–927 (2008). https://doi.org/10.1109/CEC.2008.4630906

  33. 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)

    Article  Google Scholar 

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Acknowledgments

The work presented in this paper was supported by Polish National Science Centre PRELUDIUM project no. 2017/25/N/ST6/02841.

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Authors and Affiliations

  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Krakow, Poland

    Mateusz Godzik, Michał Idzik, Kamil Pietak, Aleksander Byrski & Marek Kisiel-Dorohinicki

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  1. Mateusz Godzik
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  2. Michał Idzik
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  3. Kamil Pietak
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  4. Aleksander Byrski
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  5. Marek Kisiel-Dorohinicki
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Corresponding author

Correspondence to Mateusz Godzik .

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Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Thua Thien Hue Center of Information Technology, Hue, Vietnam

    Bao Hung Hoang

  3. Vietnam - Korea University of Information and Communication Technology, University of Da Nang, Da Nang, Vietnam

    Cong Phap Huynh

  4. Department of Computer Engineering, Yeungnam University, Gyeungsan, Korea (Republic of)

    Dosam Hwang

  5. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

  6. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Godzik, M., Idzik, M., Pietak, K., Byrski, A., Kisiel-Dorohinicki, M. (2020). Autonomous Hybridization of Agent-Based Computing. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-63007-2_11

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