Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Code Importing Techniques for Fast, Safe Client/Server Access
Code Importing Techniques for Fast, Safe Client/Server Access
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
On the Analysis of Performance of the Improved Artificial-Bee-Colony Algorithm
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Parallelization of the artificial bee colony (ABC) algorithm
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Parallelized Artificial Bee Colony with Ripple-communication Strategy
ICGEC '10 Proceedings of the 2010 Fourth International Conference on Genetic and Evolutionary Computing
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Information Sciences: an International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach
Information Sciences: an International Journal
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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.