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Parallel Multi-memetic Global Optimization Algorithm for Optimal Control of Polyarylenephthalide’s Thermally-Stimulated Luminescence

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Optimization of Complex Systems: Theory, Models, Algorithms and Applications (WCGO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 991))

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Abstract

This paper presents a modification of the parallel multi-memetic global optimization algorithm based on the Mind Evolutionary Computation algorithm which is designed for loosely coupled computing systems. The algorithm implies a two-level adaptation strategy based on the proposed landscape analysis procedure and utilization of multi-memes. It is also consistent with the architecture of loosely coupled computing systems due to the new static load balancing procedure that allows to allocate more computational resources for promising search domain’s sub-areas while maintaining approximately equal load of computational nodes. The new algorithm and its software implementation were utilized to solve a computationally expensive optimal control problem for a model of chemical reaction’s dynamic for thermally-stimulated luminescence of polyarylenephtalides. Results of the numerical experiments are presented in this paper.

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References

  1. Sakharov, M.K., Karpenko, A.P., Velisevich, Ya.I.: Multi-memetic mind evolutionary computation algorithm for loosely coupled systems of desktop computers. In: Science and Education of the Bauman MSTU, vol. 10, pp. 438–452 (2015). https://doi.org/10.7463/1015.0814435

  2. Karpenko, A.P.: Modern algorithms of search engine optimization. Nature-inspired optimization algorithms. Moscow, Bauman MSTU Publ., p. 446 (2014)

    Google Scholar 

  3. Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, pp. 368. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-23247-3

  4. Mersmann, O. et al.: Exploratory landscape analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. ACM, pp. 829–836. (2011). https://doi.org/10.1145/2001576.2001690

  5. Sakharov, M., Karpenko, A.: Multi-memetic mind evolutionary computation algorithm based on the landscape analysis. In: Theory and Practice of Natural Computing. 7th International Conference, TPNC 2018, Dublin, Ireland, 12–14 Dec 2018, Proceedings, pp. 238–249. Springer (2018).  https://doi.org/10.1007/978-3-030-04070-3

  6. Voevodin, V.V., Voevodin, Vl. V.: Parallel Computations, p. 608. BHV-Peterburg, SPb. (2004)

    Google Scholar 

  7. Sakharov, M.K., Karpenko, A. P.: Adaptive load balancing in the modified mind evolutionary computation algorithm. In: Supercomputing Frontiers and Innovations, 5(4), 5–14 (2018). https://doi.org/10.14529/jsfi180401

  8. Jie, J., Zeng, J.: Improved mind evolutionary computation for optimizations. In: Proceedings of 5th World Congress on Intelligent Control and Automation, Hang Zhou, China, pp. 2200–2204 (2004). https://doi.org/10.1109/WCICA.2004.1341978

  9. Chengyi, S., Yan, S., Wanzhen, W.: A Survey of MEC: 1998-2001. In: 2002 IEEE International Conference on Systems, Man and Cybernetics IEEE SMC2002, Hammamet, Tunisia. October 6–9. Institute of Electrical and Electronics Engineers Inc., vol. 6, pp. 445–453 (2002). https://doi.org/10.1109/ICSMC.2002.1175629

  10. Sakharov, M., Karpenko, A.: Performance investigation of mind evolutionary computation algorithm and some of its modifications. In: Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16), pp. 475–486. Springer (2016). https://doi.org/10.1007/978-3-319-33609-1_43

  11. Sakharov, M., Karpenko, A.: A new way of decomposing search domain in a global optimization problem. In: Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17), pp. 398–407. Springer (2018). https://doi.org/10.1007/978-3-319-68321-8_41

  12. Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, pp. 141–152 (2006)

    Google Scholar 

  13. Nelder, J.A., Meade, R.: A Simplex method for function minimization. Comput. J. 7, 308–313 (1965)

    Google Scholar 

  14. Karpenko, A.P.: Optimization Methods (Introductory Course), http://bigor.bmstu.ru/. Accessed 25 Mar 2019

  15. Sokolov, A.P., Pershin, A.Y.: Computer-aided design of composite materials using reversible multiscale homogenization and graph-based software engineering. Key Eng. Mater. 779, 11–18 (2018). https://doi.org/10.4028/www.scientific.net/KEM.779.11

  16. Agasiev, T., Karpenko, A.: The program system for automated parameter tuning of optimization algorithms. Proc. Comput. Sci. 103, 347–354 (2017). https://doi.org/10.1016/j.procs.2017.01.120

    Google Scholar 

  17. Antipin, V.A., Shishlov, N.M., Khursan, S.L.: Photoluminescence of polyarylenephthalides. VI. DFT study of charge separation process during polymer photoexcitation. Bulletin of Bashkir University, vol. 20, Issue 1, pp. 30–42 (2015)

    Google Scholar 

  18. Akhmetshina, L.R., Mambetova, Z.I., Ovchinnikov, M.Y.: Mathematical modeling of thermoluminescence kinetics of polyarylenephthalides. In: V International Scientific Conference on Mathematical Modeling of Processes and Systems, pp. 79–83 (2016)

    Google Scholar 

  19. Antipin, V.A., Mamykin, D.A., Kazakov, V.P.: Recombination luminescence of poly(arylene phthalide) films induced by visible light. High Energy Chem. 45(4), 352–359 (2011)

    Google Scholar 

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Acknowledgments

This work was supported by the RFBR under a grant 18-07-00341.

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Authors and Affiliations

  1. Bauman MSTU, Moscow, Russia

    Maxim Sakharov & Anatoly Karpenko

Authors
  1. Maxim Sakharov
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  2. Anatoly Karpenko
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Corresponding author

Correspondence to Maxim Sakharov .

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Editors and Affiliations

  1. Computer science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai An Le Thi

  2. Computer Science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai Minh Le

  3. Laboratory of Mathematics, National Institute for Applied Sciences (INSA)-Rouen Normadie, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh

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Sakharov, M., Karpenko, A. (2020). Parallel Multi-memetic Global Optimization Algorithm for Optimal Control of Polyarylenephthalide’s Thermally-Stimulated Luminescence. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_20

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