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Artificial Bee Colony
Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. It is as simple as Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, and uses only common control parameters such as colony size and maximum cycle number. ABC as an optimization tool, provides a population-based search procedure in which individuals called foods positions are modified by the artificial bees with time and the bee’s aim is to discover the places of food sources with high nectar amount and finally the one with the highest nectar. In ABC system, artificial bees fly around in a multidimensional search space and some (employed and onlooker bees) choose food sources depending on the experience of themselves and their nest mates, and adjust their positions. Some (scouts) fly and choose the food sources randomly without using experience. If the nectar amount of a new source is higher than that of the previous one in their memory, they memorize the new position and forget the previous one. Thus, ABC system combines local search methods, carried out by employed and onlooker bees, with global search methods, managed by onlookers and scouts, attempting to balance exploration and exploitation process.
- D. Karaboga, An idea based on honey bee swarm for numerical optimization, Erciyes University, Kayseri, Turkey, Technical Report-TR06, 2005.
- B. Basturk, D. Karaboga, An artificial bee colony (ABC) algorithm for numeric function optimization, in: IEEE Swarm Intelligence Symposium, 2006.
- D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (2007) 171–459.
- D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing 8 (2008) 687–697.
- D. Karaboga, B. Akay, A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation 214 (2009) 108–132.
- Alatas B. Chaotic bee colony algorithms for global numerical optimization[J]. Expert Systems with Applications, 2010, 37(8): 5682–5687.
- G. Zhu, S. Kwong, Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation 217 (2010)
- W.F. Gao, S.Y. Liu, L.L. Huang, A global best artificial bee colony algorithm for global optimization, Journal of Computational and Applied Mathematics 236 (2012) 2741–2753
- W.F. Gao, S.Y. Liu, L.L. Huang, A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS，10.1109/TSMCB.2012.2222373
- F. Kang, J. Li, Z. Ma, Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions, Information Sciences 181 (16)(2011) 3508–3531.
- W.F. Gao, S.Y. Liu, A modified artificial bee colony algorithm, Computers & Operations Research 39 (2012) 687–697.
- A. Barnharnsakun, T. Achalakul, B. Sirinaovakul, The best-so-far selection in artificial bee colony algorithm, Applied Soft Computing 11 (2011) 2888–2901.