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ABC-X: a generalized, automatically configurable artificial bee colony framework

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Abstract

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. Automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.

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Notes

  1. Some test functions are present in more than one benchmark set with different shift and rotate vectors. In this case, we only considered the function in the most recent benchmark set.

  2. We take here always the first configuration; as the configurations have been independently generated of each other, taking a configuration at an arbitrary, fixed position in the sequence of tuned configurations corresponds to taking a random one.

  3. Note that we do not claim that the separation of the problems as done here is the best possible. Our separation was guided by one specific, known feature of the available functions, which is, however, also a prominent and common one used to classify the problems in the various benchmark sets. Further work should examine alternative ways of classifying the benchmark problems, which could be exploiting other features and landscape features such as those examined by Mersmann et al. (2011).

  4. We have used the Wilcoxon test to determine whether statistically significant differences arise on each function for each dimension. If no statistically significant difference is observed, this is reported as draw.

  5. Note that for the tuning, we did not use the default ABC algorithm parameter settings as one of the initial configurations, although this could be done with irace.

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Acknowledgements

This work was supported by the Scientific and Technical Research Council of Turkey (TUBITAK) and by the COMEX Project (P7/36) within the Inter-university Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a senior research associate.

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Authors and Affiliations

  1. Computer Engineering Department, Dumlupinar University, 43000, Kutahya, Turkey

    Doğan Aydın & Gürcan Yavuz

  2. IRIDIA, CoDE, Université Libre de Bruxelles (ULB), Brussels, Belgium

    Thomas Stützle

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  1. Doğan Aydın
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  2. Gürcan Yavuz
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  3. Thomas Stützle
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Correspondence to Thomas Stützle.

Additional information

The rational underlying the design of ABC-X has, in part, been presented in an earlier conference paper, which was submitted accompanying our submission for the CEC’15 benchmark competition on learning-based real-parameter single-objective optimization (Aydın and Stützle 2015). The contribution of that paper is discussed in the “Related work and discussion” section of the present article.

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Aydın, D., Yavuz, G. & Stützle, T. ABC-X: a generalized, automatically configurable artificial bee colony framework. Swarm Intell 11, 1–38 (2017). https://doi.org/10.1007/s11721-017-0131-z

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