Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm

Despite the efficiency of evolutionary algorithms is prominent for large scale problems, their running times in terms of CPU time are quite large. Multi processing units served by recent hardware developments can be employed to overcome this drawback reducing the running time and sharing the total workload. However, evolutionary algorithms cannot be directly distributed to processing units due to their cooperative working models. These models need to be modified to be able to run them on distributed environments without causing deterioration in performance. In this study, a detailed performance analysis of a parallel model for the artificial bee colony algorithm, which is one of the recently developed swarm based evolutionary algorithms and a promising numerical optimization tool, is proposed. For this purpose large-scale benchmark problems are solved by the proposed model and also its original sequential counterpart model. The model is also applied to a real-world problem: training of neural networks for classification purposes. Comparative results show that the artificial bee colony algorithm is very suitable to use in parallel architectures since it has the ability to produce high quality solutions with small populations due to its perturbation operator. The proposed model decreases the running time in addition to improving the performance and convergence rate of the algorithm. It can be said that the speedup gained over its sequential counterpart is almost linear. (C) 2013 Elsevier Inc. All rights reserved.