Exploration–exploitation balance in Artificial Bee Colony algorithm: a critical analysis
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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.
KeywordsABC 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 http://mf.erciyes.edu.tr/abc/software.htm and http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared Documents/Forms/AllItems.aspx, respectively. The authors are also grateful to editorial team and anonymous reviewers for critical comments and valuable suggestions.
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Conflict of interest
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.
- Hadidi A, Azad SK, Azad SK (2010) Structural optimization using artificial bee colony algorithm. In: 2nd international conference on engineering optimization, Lisbon, PortugalGoogle Scholar
- Karaboga D (2005) An idea based on honeybee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
- Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014. Special session and competition on single objective real-parameter numerical optimization. Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, SingaporeGoogle Scholar