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DEVELOPMENTS IN ARTIFICIAL BEE COLONY ALGORITHM AND THE RESULTS

Yıl 2020, Sayı: 1, 99 - 115, 13.01.2020

Öz

In optimization
algorithms, it is generally necessary to analyze whether it provides good
performance in most problem types and to analyze its behavior by comparing with
the algorithms in the literature. For this reason, performance analysis of
Artificial Bee Colony (ABC) algorithm, which is one of the optimization types
modeling the bee's food search behaviors, have been made.

 





The ABC, discovered by Karaboğa in 2005, is a good
mechanism for finding new solutions. However, there is a need for improvements
in ABC with respect to local research. The classical state of the algorithm and
the developed processes have been examined by taking into account the specific
parameters in the test problems and it has been shown that which improvement
produces better solutions than standard ABC.

Kaynakça

  • • AKAY, B., (2009), Nümerik Optimizasyon Problemlerinde Yapay Arı Kolonisi (Artificial Bee Colony) Algoritmasının Performans Analizi Ek-5 ABC Algoritması Kodları, Doktora Tezi, Erciyes Üniversitesi, Fen Bilimleri Enstitüsü 10-25.
  • • ARORA, J. S., (1989), Introduction Optimum Design , Mcgraw Hill 18.
  • • BABAYİĞİT, B., R. ÖZDEMİR, (2012), A Modified Artificial Bee Colony Algorithm for Numerical Function Optimization, Computers and Communications (ISCC), IEEE Symposium on 245-249.
  • • BONABEAU, E., M. DORIGO, G. THERAULAZ, (1999), Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Ny,92.
  • • DUMITRESCU, I., STUTZLE, T., (2003), Combinations of Local Serach and Exact Algorithms, Aplications of Evalotionary Computing, LNCS, Volume 2611/2003, 57-68.
  • • EIBEN, A., SMITH, J., (2003), Intorduction to Evolutionary Computing, Springer 347-365.
  • • GAO, W., S. LIU and L. HUANG, (2013), A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning, Ieee Transactions on Cybernetics, Vol. 43, No. 3, June,1011-1024.
  • • JEYA, D., V. MOHAN, M. KAMALAPRIYA, (2010), Automated Software Test Optimisation Framework – An Artificial Bee Colony Optimisation-Based Approach, IET Softw. , Vol. 4, Iss. 5, Pp. 334–348.
  • • KARABOĞA, D., (2011), Yapay Zeka Optimizasyon Algoritmaları, 202-221.
  • • KARABOĞA, D. , AKAY B., (2009), A Comparative Study of Artificial Bee Colony Algorithm, Applied Mathematics and Computation 108–132.
  • • KORB, K., RANDALL, M., HENDTLASS, T., (2009), Artificiallife: Borrowing from Biology, Springer, 211-220.
  • • LIU, J., J. WANG, B. FENG, J. HUO, (2012), Research on the Solving of Nonlinear Equation Group Based on Artificial Bee Colony Algorithm, The 7th International Conference on Computer Science & Education, July 14-17, Melbourne, Australia, 75-79.
  • • PACURIB, J., G. MAE, M. SENO, J. P. T. YUSIONG, (2009), Solving Sudoku Puzzles Using Improved Artificial Bee Colony Algorithm, Fourth International Conference on Innovative Computing Information and Control, 885-889.
  • • SUNDARESWARAN, K., P. SANKAR, P. NAYAK, S. SIMON, A. PALANI, (2015), Enhanced Energy Output from a PV System Under Partial Shaded Conditions Through Artificial Bee Colony, Ieee Transactıons on Sustainable Energy, Vol. 6, No. 1, January, 198-209.
  • • TERESHKO, V., (2000), Reaction–Diffusion Model of a Honeybee Colony’s Foraging Behaviour, in: Parallel Problem Solving from Nature PPSN VI, Lecture Notes in Computer Science, Vol. 1917, Springer–Verlag, Berlin, Pp. 807–816.
  • • TERESHKO, V., A. LOENGAROV, (2005), Collective Decision-Making in Honeybee Foraging Dynamics, Computing and Information Systems Journal 9 (3). • WAIBEL, M., (2006), Divison of Labour and Colony Efficiency in Social Insects, Proceedings of the Royal Society B., 273, 1815-23.
  • • ZHANG, X., S.YUEN, S. HO, W. FU, (2013), An Improved Artificial Bee Colony Algorithm for Optimal Design of Electromagnetic Devices, IEEE Transactions on Magnetics, Vol. 49, 4811-4816.

YAPAY ARI KOLONİSİ ALGORİTMASI İLE YAPILAN GELİŞTİRMELER VE SONUÇLARI

Yıl 2020, Sayı: 1, 99 - 115, 13.01.2020

Öz

Optimizasyon
algoritmalarında genel olarak çoğu problem türünde iyi performans sağlayıp
sağlayamadığının analiz edilmesi ve literatürdeki algoritmalarla kıyaslanarak
davranışlarının incelenmesi gerekir. Bu nedenle optimizasyon türlerinden biri
olan ve arıların yiyecek arama davranışlarını modelleyen Yapay Arı Kolonisi (ABC)
algoritmasının ilk literatüre girişinden son zamanlardaki gelişim sürecine
kadar Performans Analizi yapılmıştır.

 





Karaboğa
tarafından 2005 yılında ortaya çıkarılan ABC’nin son yıllarda yapılan
çalışmalar sonucunda yeni çözümleri bulma mekanizmasının çok iyi olduğu fakat
yerel araştırma yapma mekanizmasının geliştirilebileceğini ortaya koymuştur. Algoritmanın
klasik hali ve geliştirilen süreçler test problemlerinde belirli parametreler
dikkate alınarak incelenmiş ve hangi iyileştirmenin standart ABC’ye göre daha
iyi çözümler ürettiği gösterilmiştir.

 






Kaynakça

  • • AKAY, B., (2009), Nümerik Optimizasyon Problemlerinde Yapay Arı Kolonisi (Artificial Bee Colony) Algoritmasının Performans Analizi Ek-5 ABC Algoritması Kodları, Doktora Tezi, Erciyes Üniversitesi, Fen Bilimleri Enstitüsü 10-25.
  • • ARORA, J. S., (1989), Introduction Optimum Design , Mcgraw Hill 18.
  • • BABAYİĞİT, B., R. ÖZDEMİR, (2012), A Modified Artificial Bee Colony Algorithm for Numerical Function Optimization, Computers and Communications (ISCC), IEEE Symposium on 245-249.
  • • BONABEAU, E., M. DORIGO, G. THERAULAZ, (1999), Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Ny,92.
  • • DUMITRESCU, I., STUTZLE, T., (2003), Combinations of Local Serach and Exact Algorithms, Aplications of Evalotionary Computing, LNCS, Volume 2611/2003, 57-68.
  • • EIBEN, A., SMITH, J., (2003), Intorduction to Evolutionary Computing, Springer 347-365.
  • • GAO, W., S. LIU and L. HUANG, (2013), A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning, Ieee Transactions on Cybernetics, Vol. 43, No. 3, June,1011-1024.
  • • JEYA, D., V. MOHAN, M. KAMALAPRIYA, (2010), Automated Software Test Optimisation Framework – An Artificial Bee Colony Optimisation-Based Approach, IET Softw. , Vol. 4, Iss. 5, Pp. 334–348.
  • • KARABOĞA, D., (2011), Yapay Zeka Optimizasyon Algoritmaları, 202-221.
  • • KARABOĞA, D. , AKAY B., (2009), A Comparative Study of Artificial Bee Colony Algorithm, Applied Mathematics and Computation 108–132.
  • • KORB, K., RANDALL, M., HENDTLASS, T., (2009), Artificiallife: Borrowing from Biology, Springer, 211-220.
  • • LIU, J., J. WANG, B. FENG, J. HUO, (2012), Research on the Solving of Nonlinear Equation Group Based on Artificial Bee Colony Algorithm, The 7th International Conference on Computer Science & Education, July 14-17, Melbourne, Australia, 75-79.
  • • PACURIB, J., G. MAE, M. SENO, J. P. T. YUSIONG, (2009), Solving Sudoku Puzzles Using Improved Artificial Bee Colony Algorithm, Fourth International Conference on Innovative Computing Information and Control, 885-889.
  • • SUNDARESWARAN, K., P. SANKAR, P. NAYAK, S. SIMON, A. PALANI, (2015), Enhanced Energy Output from a PV System Under Partial Shaded Conditions Through Artificial Bee Colony, Ieee Transactıons on Sustainable Energy, Vol. 6, No. 1, January, 198-209.
  • • TERESHKO, V., (2000), Reaction–Diffusion Model of a Honeybee Colony’s Foraging Behaviour, in: Parallel Problem Solving from Nature PPSN VI, Lecture Notes in Computer Science, Vol. 1917, Springer–Verlag, Berlin, Pp. 807–816.
  • • TERESHKO, V., A. LOENGAROV, (2005), Collective Decision-Making in Honeybee Foraging Dynamics, Computing and Information Systems Journal 9 (3). • WAIBEL, M., (2006), Divison of Labour and Colony Efficiency in Social Insects, Proceedings of the Royal Society B., 273, 1815-23.
  • • ZHANG, X., S.YUEN, S. HO, W. FU, (2013), An Improved Artificial Bee Colony Algorithm for Optimal Design of Electromagnetic Devices, IEEE Transactions on Magnetics, Vol. 49, 4811-4816.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Burak Kaya

Yayımlanma Tarihi 13 Ocak 2020
Gönderilme Tarihi 6 Mayıs 2018
Yayımlandığı Sayı Yıl 2020 Sayı: 1

Kaynak Göster

APA Kaya, B. (2020). YAPAY ARI KOLONİSİ ALGORİTMASI İLE YAPILAN GELİŞTİRMELER VE SONUÇLARI. Verimlilik Dergisi(1), 99-115.

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