Title
Performance evaluation of artificial bee colony optimization and new selection schemes.
Abstract
The artificial bee colony optimization (ABC) is a population-based algorithm for function optimization that is inspired by the foraging behavior of bees. The population consists of two types of artificial bees: employed bees (EBs) which scout for new, good solutions and onlooker bees (OBs) that search in the neighborhood of solutions found by the EBs. In this paper we study in detail the influence of ABC’s parameters on its optimization behavior. It is also investigated whether the use of OBs is always advantageous. Moreover, we propose two new variants of ABC which use new methods for the position update of the artificial bees. Extensive empirical tests were performed to compare the new variants with the standard ABC and several other metaheuristics on a set of benchmark functions. Our findings show that the ideal parameter values depend on the hardness of the optimization goal and that the standard values suggested in the literature should be applied with care. Moreover, it is shown that in some situations it is advantageous to use OBs but in others it is not. In addition, a potential problem of the ABC is identified, namely that it performs worse on many functions when the optimum is not located at the center of the search space. Finally it is shown that the new ABC variants improve the algorithm’s performance and achieve very good performance in comparison to other metaheuristics under standard as well as hard optimization goals.
Year
DOI
Venue
2011
10.1007/s12293-011-0065-8
Memetic Computing
Keywords
Field
DocType
Swarm intelligence, Artificial bee colony optimization, Function optimization
Population,Artificial bee colony algorithm,Mathematical optimization,Swarm intelligence,Function optimization,Artificial intelligence,Bees algorithm,Artificial bee colony optimization,Foraging,Mathematics,Metaheuristic
Journal
Volume
Issue
ISSN
3
3
1865-9292
Citations 
PageRank 
References 
52
1.42
24
Authors
4
Name
Order
Citations
PageRank
Konrad Diwold1694.94
Andrej Aderhold2694.06
Alexander Scheidler318216.52
Martin Middendorf41334161.45