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Swarm Intelligence

, Volume 7, Issue 4, pp 327–356 | Cite as

Artificial bee colonies for continuous optimization: Experimental analysis and improvements

  • Tianjun Liao
  • Doğan Aydın
  • Thomas Stützle
Article

Abstract

The artificial bee colony (ABC) algorithm is a recent class of swarm intelligence algorithms that is loosely inspired by the foraging behavior of honeybee swarms. It was introduced in 2005 using continuous optimization problems as an example application. Similar to what has happened with other swarm intelligence techniques, after the initial proposal, several researchers have studied variants of the original algorithm. Unfortunately, often these variants have been tested under different experimental conditions and different fine-tuning efforts for the algorithm parameters. In this article, we review various variants of the original ABC algorithm and experimentally study nine ABC algorithms under two settings: either using the original parameter settings as proposed by the authors, or using an automatic algorithm configuration tool using a same tuning effort for each algorithm. We also study the effect of adding local search to the ABC algorithms. Our experimental results show that local search can improve considerably the performance of several ABC variants and that it reduces strongly the performance differences between the studied ABC variants. We also show that the best ABC variants are competitive with recent state-of-the-art algorithms on the benchmark set we used, which establishes ABC algorithms as serious competitors in continuous optimization.

Keywords

Artificial bee colony Continuous optimization Experimental study Automatic algorithm configuration 

Notes

Acknowledgements

The research leading to the results presented in this paper has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement No. 246939. This work was also supported by the META-X project, an Action de Recherche Concertée funded by the Scientific Research Directorate of the French Community of Belgium. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Associate. Tianjun Liao acknowledges a fellowship from the China Scholarship Council. We also acknowledge the detailed comments by the editor and the referees that helped to improve considerably the paper.

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.IRIDIA, CoDEUniversité Libre de Bruxelles (ULB)BrusselsBelgium
  2. 2.Computer Engineering Dept.Dumlupinar UniversityKütahyaTurkey

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