μABC: a micro artificial bee colony algorithm for large scale global optimization

Authors:
Anguluri Rajasekhar;Swagatam Das;Sanjoy Das
Affiliations:
NIT, Warangal, India, Warangal, India;Indian Statistical Institute, Kolkata, Kolkata, India;Kansas State University, Manhattan, KS, USA
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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Abstract

In this paper, we propose a new variant of Artificial Bee Colony Algorithm termed as mABC: Micro Artificial Bee Colony algorithm, which evolves with a very small population compared to its traditional version. In this approach the bees are ranked via their fitness. Best bee is kept unaltered, whereas the other bees are reinitialized with help of some modifications based on the food source obtained by best bee. This type of raking system will always help bees (apart from best bee) to exploit areas in the vicinity of food source corresponding to best bee. mABC is validated over a benchmark suite of shifted functions suggested in CEC'2008 competition and compared with the methods like EPS-PSO, CCPSO2, etc. Various comparisons with dimensions greater than 100 show the performance of mABC in solving higher dimensional problems with less computational effort.