Artificial Bee Colony algorithm with improved search mechanism

Abstract

In a preceding study, authors critically analysed the functional behaviour of Artificial Bee Colony (ABC) algorithm in view of some of its reported search limitations and offered directions for performance improvements. Accordingly, an improved ABC (IABC) algorithm is proposed with inclusion of three features: (1) dynamic update of probability values of food sources after every successful new search under the onlooker bees operator, (2) Allocation of variable ‘effective limit’ to each food source based upon its food quality instead of global fixed ‘limit’ and (3) insulation of best-so-far solution from scout bee operator. The additional features render substantial improvements in search abilities of ABC algorithm. The experiments with classical and CEC’2014 benchmark test functions confirm the supremacy of IABC algorithm over basic ABC algorithm as well as some of its variants and other evolutionary algorithms. Notably, the IABC algorithm does not introduce any new control parameter or hybridization with any other operator and maintains almost same level of computational complexity.

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Acknowledgements

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/SharedDocuments/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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Correspondence to Amreek Singh.

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Singh, A., Deep, K. Artificial Bee Colony algorithm with improved search mechanism. Soft Comput 23, 12437–12460 (2019). https://doi.org/10.1007/s00500-019-03785-y

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Keywords

  • ABC algorithm
  • CEC’2014 benchmark test suite
  • Metaheuristic
  • Numerical optimization
  • Wilcoxon’s signed-rank test
  • Friedman test