The artificial bee colony (ABC) algorithm is a swarm intelligence algorithm inspired by the intelligent foraging behavior of a honeybee swarm. In recent years, several ABC variants that modify some components of the original ABC algorithm have been proposed. Although there are some comparison studies in the literature, the individual contribution of each proposed modification is often unknown. In this paper, the proposed modifications are tested with a systematic experimental study that by a component-wise analysis tries to identify their impact on algorithm performance. This study is done on two benchmark sets in continuous optimization. In addition to this analysis, two new variants of ABC algorithms for each of the two benchmark sets are proposed. To do so, the best components are selected for each step of the Composite ABC algorithms. The performance of the proposed algorithms were compared against that of ten recent ABC algorithms, as well as against several recent state-of-the-art algorithms. The comparison results showed that our proposed algorithms outperform other ABC algorithms. Moreover, the composite ABC algorithms are superior to several state-of-the-art algorithms proposed in the literature. 
Composite artificial bee colony algorithms: From component-based analysis to high-performing algorithms