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Differential Evolution Algorithm and Its Variants

Yıl 2017, Cilt: 2 Sayı: 1, 10 - 14, 01.06.2017

Öz

Differential
evolution (DE) is a popular population-based stochastic meta-heuristic method.
There are many meta-heuristic methods with different names such as League
Championship Algorithm, Artificial Bee Algorithm, Bee Swarm Optimization, Cat
Swarm Optimization, Differential Search, Goose Optimization Algorithm. In this
paper, the similarities of all these methods were discussed.

Kaynakça

  • [1] A. Karcı, “Saplings sowing and growing up algorithm convergence properties”,INISTA 2007 International Symposium on Innovations in Intelligent Systems and Applications, pp.322-326, 2007.
  • [2] A. Karcı, A. Arslan, “Uniform Population in Genetic Algorithms”, Journal of Electrical and Electronics, Vol.2, pp.495-504, 2002.
  • [3] S. Łukasik, S. Zak, “Firefly algorithm for continuous constrained optimization tasks”, In Computational Collective Intelligence. Semantic Web, Social Networks and MultiagentSystems, LNCS, Vol. 5796, pp. 97–106, 2009.
  • [4] M. Canayaz, A. Karcı, “Cricket behaviour based evolutionary computation technique in solving engineering optimization problems”, Applied Intelligence, Vol.44, pp.362-376., 2016.
  • [5] R.Storn, K.Price, “Heuristic for global optimization over continuous spaces”, Journal of Global Optimization, Vol.11, pp.341-359, 1995.
  • [6] A.H. Kashan, “League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships”, Applied Soft Computing, Vol.16, pp.171-200, 2014.
  • [7] D.Karaboğa, B.Baştürk, “A powerful and efficient algorithm for numerical function ptimization: artificial bee colony (ABC) algorithm”, Journal of Global Optimization, Vol.39, pp.459-471, 2007.
  • [8] R.Akbari, A.Mohammadi, K.Ziarati, “A powerful bee swarm optimization algorithm”, In 13th International Multitopic Conference (INMIC), pp.1-6, 2009.
  • [9] S.C. Chu, O.W. Tsai, "Computational intelligence based on the behavior of cats", International Journal of Innovative Computing, Information and Control, Vol.3, pp.163-173, 2007.
  • [10] P. Civicioğlu, " Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm", Computers and Geosciences, Vol.46, pp.229-247, 2012.
  • [11] J.Y. Liu, M.Z.Guo, C.Deng, “G. Hagler, “Geese PSO: An efficient improvement to particle swarm optimization”, Computer Science, Vol.33, pp.166-168, 2006.

Differential Evolution Algorithm and Its Variants

Yıl 2017, Cilt: 2 Sayı: 1, 10 - 14, 01.06.2017

Öz

Differential
evolution (DE) is a popular population-based stochastic meta-heuristic method.
There are many meta-heuristic methods with different names such as League
Championship Algorithm, Artificial Bee Algorithm, Bee Swarm Optimization, Cat
Swarm Optimization, Differential Search, Goose Optimization Algorithm. In this
paper, the similarities of all these methods were discussed.

Kaynakça

  • [1] A. Karcı, “Saplings sowing and growing up algorithm convergence properties”,INISTA 2007 International Symposium on Innovations in Intelligent Systems and Applications, pp.322-326, 2007.
  • [2] A. Karcı, A. Arslan, “Uniform Population in Genetic Algorithms”, Journal of Electrical and Electronics, Vol.2, pp.495-504, 2002.
  • [3] S. Łukasik, S. Zak, “Firefly algorithm for continuous constrained optimization tasks”, In Computational Collective Intelligence. Semantic Web, Social Networks and MultiagentSystems, LNCS, Vol. 5796, pp. 97–106, 2009.
  • [4] M. Canayaz, A. Karcı, “Cricket behaviour based evolutionary computation technique in solving engineering optimization problems”, Applied Intelligence, Vol.44, pp.362-376., 2016.
  • [5] R.Storn, K.Price, “Heuristic for global optimization over continuous spaces”, Journal of Global Optimization, Vol.11, pp.341-359, 1995.
  • [6] A.H. Kashan, “League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships”, Applied Soft Computing, Vol.16, pp.171-200, 2014.
  • [7] D.Karaboğa, B.Baştürk, “A powerful and efficient algorithm for numerical function ptimization: artificial bee colony (ABC) algorithm”, Journal of Global Optimization, Vol.39, pp.459-471, 2007.
  • [8] R.Akbari, A.Mohammadi, K.Ziarati, “A powerful bee swarm optimization algorithm”, In 13th International Multitopic Conference (INMIC), pp.1-6, 2009.
  • [9] S.C. Chu, O.W. Tsai, "Computational intelligence based on the behavior of cats", International Journal of Innovative Computing, Information and Control, Vol.3, pp.163-173, 2007.
  • [10] P. Civicioğlu, " Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm", Computers and Geosciences, Vol.46, pp.229-247, 2012.
  • [11] J.Y. Liu, M.Z.Guo, C.Deng, “G. Hagler, “Geese PSO: An efficient improvement to particle swarm optimization”, Computer Science, Vol.33, pp.166-168, 2006.
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Bölüm PAPERS
Yazarlar

Ali Karcı

Yayımlanma Tarihi 1 Haziran 2017
Gönderilme Tarihi 9 Ağustos 2017
Kabul Tarihi 30 Mayıs 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 2 Sayı: 1

Kaynak Göster

APA Karcı, A. (2017). Differential Evolution Algorithm and Its Variants. Computer Science, 2(1), 10-14.

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