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A Hybrid Differential Evolution-Gradient Optimization Method

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Artificial Intelligence and Soft Computing (ICAISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

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Abstract

In this paper a new three level, hybrid optimization method is proposed. Differential evolution is hybridized with traditonal gradient optimization. Some ideas from simulated annealing are also employed. Usefulness of the proposed method is supported by numerical simulations.

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References

  1. Ahandani, M.A., Vakil-Baghmisheh, M.T., Talebi, M.: Hybridizing Local Search Algorithms for Global Optimization Computational Optimization and Applications (Article in Press) (2014)

    Google Scholar 

  2. Brownlee J.: Clever Algorithms. Nature-Inspired Programming Recipes. LuLu. (January 2011) ISBN: 978-1-4467-8506-5

    Google Scholar 

  3. Cpałka, K., Rutkowski, L.: Evolutionary Learning of Flexible Neuro-Fuzzy Structures. In: Recent Advances in Control and Automation, pp. 398–407. Akademicka Oficyna Wydawnicza EXIT (2008)

    Google Scholar 

  4. Galar, R.: Handicapped Individua in Evolutionary Processes. Biol. Cybern. 53, 1–9 (1985)

    Article  Google Scholar 

  5. Galar, R.: Evolutionary Search with Soft Selection. Biol. Cybern. 60, 357–364 (1989)

    Article  MathSciNet  Google Scholar 

  6. Gong, W., Cai, Z.: A Multiobjective Differential Evolution Algorithm for Constrained Optimization. In: 2008 IEEE Congress on Evolutionary Computation (2008)

    Google Scholar 

  7. Gordián-Rivera, L.-A., Mezura-Montes, E.: A Combination of Specialized Differential Evolution Variants for Constrained Optimization. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds.) IBERAMIA 2012. LNCS, vol. 7637, pp. 261–270. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. Lobato, F.S., Valder, S., Neto, A.: Solution of Singular Optimal Control Problems Using the Improved Differential Evolution Algorithm. Journal of Artificial Intelligence and Soft Computing Research 1(3), 195–206 (2011)

    Google Scholar 

  9. de Melo, V., Grazieli, L., Costa, C.: Evaluating differential evolution with penalty function to solve constrained engineering problems. Expert Systems with Applications 39, 7860–7863 (2012)

    Article  Google Scholar 

  10. Mezura-Montes, E., Coello, C.A.: A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems. IEEE Transactions on Evolutionary Computation 9(1), 1–17 (2005)

    Article  Google Scholar 

  11. Mezura-Montes, E., Coello Coello, C.A., Tun-Morales, E.I.: Simple Feasibility Rules and Differential Evolution for Constrained Optimization. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 707–716. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Muranaka, K., Aiyoshi, E.: Computational Properties of Hybrid Methods with PSO and DE. Electronics and Communications 97(4), 58–66 (2014) (in Japan)

    Google Scholar 

  13. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer Science, New York (2006)

    Google Scholar 

  14. Storn, R., Price, K.: Differential evolution a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report (1995)

    Google Scholar 

  15. Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  16. Price, K., Storn, R., Lampinen, J.: Differential Evolution A Practical Approach to Global Optimization. Springer, Heidelberg (2005)

    Google Scholar 

  17. Rafajłowicz, E., Styczeń, K., Rafajłowicz, W.: A modified filter SQP method as a tool for optimal control of nonlinear systems with spatio-temporal dynamics. International Journal of Applied Mathematics and Computer Science 22(2) (2012)

    Google Scholar 

  18. Rafajłowicz, E., Rafajłowicz, W.: Fletcher’s Filter Methodology as a Soft Selector in Evolutionary Algorithms for Constrained Optimization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC 2012 and SIDE 2012. LNCS, vol. 7269, pp. 333–341. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Rocha, A.M.A.C., Costa, M.F.P., Fernandes, E.M.G.P.: An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part III. LNCS, vol. 7335, pp. 57–71. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  20. Skowron, M., Styczeń, K.: Evolutionary search for globally optimal constrained stable cycles. Chemical Engineering Science 61(24), 7924–7932 (2006)

    Article  Google Scholar 

  21. Skowron, M., Styczeń, K.: Evolutionary search for globally optimal stable multicycles in complex systems with inventory couplings. International Journal of Chemical Engineering (2009)

    Google Scholar 

  22. Xue, Y., Zhong, S., Ma, T., Cao, J.: A Hybrid Evolutionary Algorithm for Numerical Optimization Problem Intelligent Automation and Soft Computing (2014) (article in Press)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Wojciech Rafajłowicz is with the Department of Computer Engineering, Faculty of Electronics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50 370, Wrocław, Poland

    Wojciech Rafajłowicz

Authors
  1. Wojciech Rafajłowicz
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Corresponding author

Correspondence to Wojciech Rafajłowicz .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Rafajłowicz, W. (2015). A Hybrid Differential Evolution-Gradient Optimization Method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_35

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_35

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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