Exploration–exploitation balance in Artificial Bee Colony algorithm: a critical analysis

  • Amreek SinghEmail author
  • Kusum Deep
Methodologies and Application


Artificial Bee Colony (ABC) algorithm is a popular metaheuristic due to its simplicity yet a stronger search mechanism. However, some researchers have reported that ABC algorithm lays more emphasis on exploration in comparison with exploitation, its performance also deteriorates gradually as the dimensions of the problems increase and the algorithm may occasionally stop proceeding towards the global optimum. Hence, the algorithm runs the risk of missing out on true global optima. This study critically analyses the functional behaviour of ABC algorithm in the context of above reports and finds that the scout bee operator may turn redundant while dealing with high dimensional problems. Thus, in contrast to the popular view, the study suggests that the ABC algorithm may be poor in exploration ability too for high-dimensional problems. Further, the study offers an explanation for the above-reported observations by other researchers. The findings of the study may be quite useful for designing better performing variants of ABC algorithm.


ABC algorithm CEC’2014 benchmark test suite Metaheuristic Numerical optimization 



The MATLAB codes of ABC algorithm and CEC’2014 benchmark test suite used in this study were downloaded from and Documents/Forms/AllItems.aspx, respectively. The authors are also grateful to editorial team and anonymous reviewers for critical comments and valuable suggestions.

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology – RoorkeeRoorkeeIndia
  2. 2.Snow and Avalanche Study EstablishmentChandigarhIndia

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