YAPAY ARI KOLONİSİ ALGORİTMASI İLE YAPILAN GELİŞTİRMELER VE SONUÇLARI

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.

Anahtar Kelimeler:

Optimizasyon, Yapay Arı Kolonisi, Test Problemleri

DEVELOPMENTS IN ARTIFICIAL BEE COLONY ALGORITHM AND THE RESULTS

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.

Keywords:

Optimization, Artificial Bee Colony, Test Problems,

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• 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.