PDF EndNote BibTex Cite

Rössler Tabanlı Kaotik Farksal Gelişim Algoritması

Year 2014, Volume 1, Issue 2, 9 - 16, 02.07.2016

Abstract

Bu çalışmada optimizasyon problemlerinin çözümünde sıklıkla kullanılan evrimsel algoritmalardan farksal gelişim algoritmasının (FGA) temelini oluşturan rastgele sayı üretim süreci yerine, Rössler tabanlı kaotik sayı üreteci önerilmiştir. Rössler sistemi Runge-Kutta yöntemi ile çözülerek elde edilen çıktılar Kaotik FGA (KFGA) yapısında kullanılmıştır. Önerilen kaotik tabanlı FGA yapısının performansını değerlendirmek için literatürden farklı özelliklere sahip on tane optimizasyon test problemi seçilmiştir. Önerilen yapının optimizasyon problemlerinin çözümünde çeşitlilik sağladığı ve test problemlerinin global minimum noktalarını başarı ile bulduğu tespit edilmiştir.

References

  • Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York, 1989.
  • Michalewicz Z., Genetic algorithms + Data structures = Evolution Programs, AI Series, SpringerVerlag,New York, 1994.
  • Holland J. H., Adaptation in Natural and Artificial Systems, Cambridge, MA:MIT Press. Second edition,1992.
  • Pham D.T. and Karaboga D., Intelligent Optimization Techniques, Springer Verlag, London, 2000.
  • Rossler O.E., "An equation for continuous chaos", Phys. Lett. A, 57, pp:397-398, 1976.
  • Rossler O.E., "An equation for hyperchaos", Phys. Lett. A, 71, pp:155-157, 1979.
  • Price K., & Storn R., "Differential evolution: A simple evolution strategy for fast optimization", Dr. Dobb’s J. Software Tools, 22 (4), pp:18-24, 1997.
  • Storn R., and Price K., "Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces", J. Global Optimization, 11, pp:341-359,1997.
  • Price K., An Introduction to Differential Evolution, D. Corne, M. Dorigo, and F. Glover, Eds. London, U.K.: McGraw-Hill, pp. 79–108, 1999.
  • Eser M. Yüzgeç U., "Kaotik Tabanlı Diferansiyel (Farksal) Gelişim Algoritması", 2nd International Symposium on Innovative Technologies in Engineering and Science, ISITES 2014, s.201-210, Karabük,18-20 Haziran 2014.
  • Yüzgeç U., Kendinden uyarlanabilir karşıtlık tabanlı farksal gelişim algoritması, Bilecik Üniversitesi, BAP Sonuç Raporu, Bilecik, 2011.
  • Yüzgeç U., "Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale fed-batch baker’s yeast fermentation process", ISA Transactions, 49(1), pp: 167-176, January 2010.

Rössler Based Chaotic Differential Evolution Algorithm

Year 2014, Volume 1, Issue 2, 9 - 16, 02.07.2016

Abstract

In this study, Rössler based the chaotic number generator was proposed instead of the random number generators which are the basis of Differential Evolution Algorithm (DE) that is the most used evolutionary algorithms in solving optimization problems. The outputs obtained by solving Rössler chaotic system with Runge-Kutta method were used in the structure Chaotic DE (CDE). For evaluating the performance of the proposed chaotic based DE structure, ten optimization test problem that have different characteristics were selected from literature. It was observed that the proposed structure provides diversity in the solution of the optimization problems and finds effectively the global minimum points of test problems.

References

  • Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York, 1989.
  • Michalewicz Z., Genetic algorithms + Data structures = Evolution Programs, AI Series, SpringerVerlag,New York, 1994.
  • Holland J. H., Adaptation in Natural and Artificial Systems, Cambridge, MA:MIT Press. Second edition,1992.
  • Pham D.T. and Karaboga D., Intelligent Optimization Techniques, Springer Verlag, London, 2000.
  • Rossler O.E., "An equation for continuous chaos", Phys. Lett. A, 57, pp:397-398, 1976.
  • Rossler O.E., "An equation for hyperchaos", Phys. Lett. A, 71, pp:155-157, 1979.
  • Price K., & Storn R., "Differential evolution: A simple evolution strategy for fast optimization", Dr. Dobb’s J. Software Tools, 22 (4), pp:18-24, 1997.
  • Storn R., and Price K., "Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces", J. Global Optimization, 11, pp:341-359,1997.
  • Price K., An Introduction to Differential Evolution, D. Corne, M. Dorigo, and F. Glover, Eds. London, U.K.: McGraw-Hill, pp. 79–108, 1999.
  • Eser M. Yüzgeç U., "Kaotik Tabanlı Diferansiyel (Farksal) Gelişim Algoritması", 2nd International Symposium on Innovative Technologies in Engineering and Science, ISITES 2014, s.201-210, Karabük,18-20 Haziran 2014.
  • Yüzgeç U., Kendinden uyarlanabilir karşıtlık tabanlı farksal gelişim algoritması, Bilecik Üniversitesi, BAP Sonuç Raporu, Bilecik, 2011.
  • Yüzgeç U., "Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale fed-batch baker’s yeast fermentation process", ISA Transactions, 49(1), pp: 167-176, January 2010.

Details

Other ID JA49AR42GG
Journal Section Articles
Authors

Uğur YÜZGEÇ This is me
?


Mehmet ESER This is me
?

Publication Date July 2, 2016
Application Date July 1, 2016
Acceptance Date
Published in Issue Year 2014, Volume 1, Issue 2

Cite

APA Yüzgeç, U. & Eser, M. (2016). Rössler Tabanlı Kaotik Farksal Gelişim Algoritması . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 1 (2) , 9-16 . Retrieved from https://dergipark.org.tr/en/pub/bseufbd/issue/22289/239061