Flag Counter
AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

A New Emigrant Creation Strategy with Randomized Sources for Parallel Artificial Bee Colony Algorithm

Paralel Yapay Arı Koloni Algoritması için Rastgele Kaynaklar ile Yeni Bir Göçmen Üretme Yaklaşımı

How to cite: Aslan S, Karaboğa D, Aksoy A. A new emigrant creation strategy with randomized sources for parallel artificial bee colony algorithm. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(1): 81-86.

Full Text: PDF, in Turkish.

Total number of downloads: 1250

Title: A New Emigrant Creation Strategy with Randomized Sources for Parallel Artificial Bee Colony Algorithm

Abstract: Dividing the whole population into subpopulations or subcolonies then evaluating them simultaneously is one of the most commonly used parallelization approaches to utilize the computational power of the current systems. However, this type of parallelization strategy decreases the population diversity because of the division of the entire population and needs migrations between subpopulations to maintain the solution diversity until the end of the iterations. In this study, we proposed a new emigrant creation strategy in which the parameters of the best food source being migrated to the neighbor subpopulation is modified with the more appropriate parameters of the randomly determined solution or solutions and investigated its effect on the performance of the parallel Artificial Bee Colony (ABC) algorithm. Experimental studies showed that newly proposed emigrant creation strategy based on randomized solutions significantly improved the convergence performance and solution qualities of parallel ABC algorithm compared to the its standard serial and ring neighborhood topology based parallel implementation for which the best solutions are directly used as emigrants.

Keywords: Artificial bee colony; parallelization


Başlık: Paralel Yapay Arı Koloni Algoritması için Rastgele Kaynaklar ile Yeni Bir Göçmen Üretme Yaklaşımı

Özet: Popülasyon tabanlı algoritmaların mevcut sistemlerin hesaplama gücünden faydalanabilmek üzere alt popülasyon ya da kolonilere ayrılıp eş zamanlı işletilmesi en sık başvurulan paralelleştirme yaklaşımları arasında yer alır. Ancak bu genel yaklaşım, popülasyonun alt popülasyonlara ayrılıyor olması sebebi ile çözüm çeşitliliğini azaltmakta ve çözüm çeşitliliğini iterasyonların sonuna kadar koruyabilmek adına alt popülasyonlar arasında çözümlerin göç ettirilmesine ihtiyaç duymaktadır. Bu çalışmada, alt popülasyonda göç ettirilmek üzere seçilen en iyi çözümün parametrelerinin aynı alt popülasyondaki rastgele belirlenmiş çözüm yada çözümlerin daha uygun parametreleri ile güncellendiği yeni bir yaklaşım önerilmiş ve bu yaklaşımın paralel Yapay Arı Koloni (Artificial Bee Colony, ABC) algoritmasının performansı üzerindeki etkileri incelenmiştir. Uygulama sonuçları, rastgele çözüm destekli yeni göçmen üretme stratejisinin paralel Yapay Arı Koloni algoritmasının yakınsama performansı ve çözüm kalitesini, seri ABC algoritması ve doğrudan en iyi çözümün göçmen olarak seçildiği ring komşuluk topolojili paralel ABC algoritmasına göre önemli oranlarda iyileştirildiğini göstermiştir.

Anahtar kelimeler: Yapay arı kolonisi; paralelleştirme


Bibliography:
  • Kumar B, Kumar D. A review on artificial bee colony algorithm. International Journal of Engineering & Technologies 2013; 2(3): 1030.
  • Bansal JC, Sharma H, Jadon SS. Artificial bee colony algorithm: A survey. International Journal of Advanced Intelligence Paradigm 2013; 5(1-2): 123-159.
  • Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA. Artificial bee colony algorithm, its variants and applications: A survey. Journal of Theoretical & Applied Information Technology 2013; 47(2): 434-459.
  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N. A comprehensive survey: Artificial bee colony algorithm and applications. Artificial Intelligence Review 2014; 42(1): 21-57.
  • Akay B, Karaboga D. A survey on the applications of artificial bee colony in signal, image and video processing. Signal, Image and Video Processing 2015; 9(4): 967-990.
  • Marinakis Y, Marinaki M, Matsatsinis N. A hybrid discrete artificial bee colony-GRASP algorithm for clustering. In International Conference on Computers & Industrial Engineering, Troyes, Fransa, 2009.
  • Zhao H, Pei Z, Jiang J, Guan R, Wang C, Shi X. A hybrid swarm intelligent method based on genetic algorithm and artificial bee colony. In Proceedings of the First international conference on Advances in Swarm Intelligence-Volume Part I, 2010, pp. 558-565.
  • Xu C, Duan H, Liu F. Chaotic artificial bee colony approach to uninhabited combat air vehicle (UCAV) path planning. Aerospace Science and Technology 2010; 14(8): 535-541.
  • Kiran MS, Gunduz M. A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Applied Soft Computing 2013; 13(4): 2188-2203.
  • Tsai P, Pan JS, Liao BY, Chu SC. Enhanced artificial bee colony optimization. International Journal of Innovative Computing, Information and Control 2009; 5(12): 5081-5092.
  • Pan QK, Tasgetiren MF, Suganthan PN, Chua PN. A discrete artificial bee colony algorithm for the lot-streaming flow show scheduling problem. Information Sciences 2011; 181(12): 2455-2468.
  • Coelho S, Alotto L. Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem. IEEE Transactions on Magnetics 2011; 47(5): 1326-1329.
  • Li J, Xie S, Pan Q, Wang S. A hybrid artificial bee colony algorithm for flexible job scheduling problems. Interational Journal of Computers Communications & Control 2011; 6(2): 286-296.
  • Narasimhan H. Parallel artificial bee colony (PABC) algorithm. In World Congress on Nature & Biologically Inspired Computing, Coimbatore, Hindistan, 2009.
  • Banharnsakun A, Achalakul T, Sirinaovakul B. Artificial bee colony algorithm on distributed environments. In Second World Congress on Nature & Biologically Inspired Computing, Kitakyushu, Japonya, 2010.
  • Basturk A, Akay R. Parallel implementation of synchronous type artificial bee colony algorithm for global optimization. Journal of Optimization Theory and Applicatons 2012; 155(3): 1095-1104.
  • Basturk A, Akay R. Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm. Information Sciences 2013; 253: 34-55.
  • Karaboga D, Aslan S. Best supported emigrant creation for parallel implementation of artificial bee colony algorithm. IU-Journal of Electrical & Electronics Engineering 2016; 16(2): 2055-2064.
  • Aslan S, Badem H, Karaboga D, Basturk A. A new sychronous parallel artificial bee colony algorithm. In 1st International Conference on Engineering Technology and Applied Sciences, Afyonkarahisar, Turkey, 2016.
  • Batbat T, Ozturk C. Protein structure prediction with discrete artificial bee colony algorithm. International Journal of Informatics Technologies 2016; 9(3): 263.
  • Badem H, Basturk A, Caliskan A, Yuksel M. A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory BFGS optimization algorithms. Neurocompuing 2017; 266: 506-526.