Paper Info
Title
μABC: a micro artificial bee colony algorithm for large scale global optimization
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.
Year
DOI
Venue
2012
10.1145/2330784.2330951
GECCO (Companion)
Keywords
Field
DocType
large scale,new variant,global optimization,various comparison,micro artificial bee colony,best bee,food source,higher dimensional problem,small population,artificial bee colony algorithm,benchmark suite,computational effort,ant colony optimization,traveling salesman problem
Ant colony optimization algorithms,Population,Artificial bee colony algorithm,Global optimization,Ranking,Computer science,Travelling salesman problem,Artificial intelligence,Bees algorithm,Machine learning
Conference
Citations 
PageRank 
References 
3
0.41
3
Authors
3
Name
Order
Citations
PageRank
Anguluri Rajasekhar112011.05
Swagatam Das26026276.66
Sanjoy Das322639.18