Improved quick artificial bee colony (iqABC) algorithm for global optimization
- 273 Downloads
Artificial bee colony (ABC) algorithm inspired by the complex behaviors of honey bees in foraging is one of the most significant swarm intelligence-based meta-heuristics and has been successfully applied to a number of numerical and combinatorial optimization problems. In this study, for increasing the early convergence performance of the ABC algorithm while protecting the qualities of the final solutions, a new exploitation mechanism from the best food source that is managed by the number of evaluations is described and its efficiency on both employed and onlooker bee phases is analyzed. The results of the experimental studies obtained from a set of benchmark problems showed that the ABC algorithm with the proposed method performs significantly better than the standard implementation of ABC algorithm and its other variants in terms of convergence speed and solution quality especially for the difficult problems that should be solved before completion of the relatively small number of fitness evaluations.
KeywordsSwarm intelligence Artificial bee colony Convergence speed
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Banharnsakun A, Achalakul T, Sirinaovakul B (2010) Artificial bee colony algorithm on distributed environment. In: Second world congress on nature and biologically inspired computing. IEEE, pp 13–18Google Scholar
- Bansal JC, S H, Jadon S (2013) Artificial bee colony algorithm: a survey. Int J Adv Intell 5(1–2):123–159Google Scholar
- Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA (2013) Artificial bee colony algorithm, its variants and applications: a survey. J Theor Appl Inf Technol 47(2):434–459Google Scholar
- Chen Q, Liu B, Zhang Q, Liang J, Suganthan P, Qu B (2015) Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization. In: 2015 IEEE congress on evolutionary computation (CEC), pp 84–88. https://doi.org/10.1109/CEC.2011.5949602
- Dorigo M, Birattari M (2011) Ant colony optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, pp 36–39Google Scholar
- Karaboga D, Akay B (2007) Artificial bee colony algorithm for training feed forward neural networks. In: IEEE 15th signal processing and communication applications conference. IEEE, pp 1–4Google Scholar
- Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, pp 760–766Google Scholar
- Mala DJ, Mohan V (2009) ABC tester-artificial bee colony based software test suite optimization approach. Int J Softw Eng 02(02):15–43Google Scholar
- Mini S, Udgata S.K, Sabat S.K (2010) Sensor deployment in 3-D terrain using artificial bee colony algorithm. In: International conference on swarm, evolutionary, and memetic computing. Springer, pp 424–431Google Scholar
- Narasimhan N (2009) Parallel artificial bee colony algorithm. In: World congress on nature and biologically inspired computing. IEEE, pp 306–311Google Scholar
- Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. In: 2011 IEEE congress on evolutionary computation (CEC). IEEE, pp 84–88Google Scholar
- Tsai PW, Pan JS, Liao BY, Chu SC (2009) Enhanced artificial bee colony optimization. Int J Innov Comput Inf Control 5(12):5081–5092Google Scholar
- Udgata SK, Sabat SL, Mini S (2009) Sensor deployment in irregular terrain using artificial bee colony algorithm. In: World congress on nature & biologically inspired computing, 2009. NaBIC, pp 1309–1314Google Scholar
- Yan X, Zhu Y, Zou W (2011) A hybrid artificial bee colony algorithm for numerical function optimization. In: 2011 11th international conference on hybrid intelligent systems (HIS). IEEE, pp 127–132Google Scholar