Parallel Implementation of Synchronous Type Artificial Bee Colony Algorithm for Global Optimization
- 537 Downloads
Evolutionary algorithms often need huge running times when solving large-scale optimization problems. One of the solutions for this issue is to introduce parallelization into the algorithm. To benefit from this approach for the artificial bee colony optimization algorithm, we present a new synchronous and parallel version of the algorithm. Performances of the proposed version and the original asynchronous algorithm are compared in terms of efficiency and speedup. Algorithms are competed to solve 20 large-scale global optimization problems. Comparative results show that the proposed parallel algorithm is still efficient as asynchronous version while it requires much less time to solve complex and large problems.
KeywordsParallel algorithms Parallel computing Artificial bee colony optimization algorithm Global optimization
The numerical calculations reported in this paper are performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TR-Grid e-Infrastructure).
This study is a part of the research project under the contract number FBA-11-3520 and the authors would like to thank Erciyes University, Scientific Research Projects Unit.
- 3.Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Tech. Report No. TR06, Erciyes University (2005) Google Scholar
- 5.Luo, R., Pan, S.T., Tsai, P.W., Pan, J.S.: Parallelized artificial bee colony with ripple-communication strategy. In: Fourth International Conference on Genetic and Evolutionary Computing, pp. 350–353 (2010) Google Scholar
- 6.Subotic, M., Tuba, M., Stanarevic, N.: Parallelization of the artificial bee colony (ABC) algorithm. In: 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, pp. 191–196 (2010) Google Scholar
- 7.Subotic, M., Tuba, M., Stanarevic, N.: Different approaches in parallelization of the artificial bee colony algorithm. Int. J. Math. Models Methods Appl. Sci. 5(4), 755–762 (2011) Google Scholar
- 9.Parpinelli, R.S., Benitez, C.M.V., Lopes, H.S.: Parallel approaches for the artificial bee colony algorithm. In: Panigrahi, B.K., Shi, Y., Lim, M.H., Hiot, L.M., Ong, Y.S. (eds.) Handbook of Swarm Intelligence, Adaptation, Learning, and Optimization, vol. 8, pp. 329–345. Springer, Berlin (2010) Google Scholar
- 10.Gabriel, E., Fagg, G.E., Bosilca, G., Angskun, T., Dongarra, J.J., Squyres, J.M., Sahay, V., Kambadur, P., Barrett, B., Lumsdaine, A., Castain, R.H., Daniel, D.J., Graham, R.L., Woodall, T.S.: Open MPI: goals, concept, and design of a next generation MPI implementation. In: 11th European PVM/MPI Users’ Group Meeting Proceedings, pp. 97–104 (2004) Google Scholar
- 12.Tang, K., Li, X., Suganthan, P.N., Weise, T.: Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Tech. report at http://nical.ustc.edu.cn/cec10ss.php, Nature Inspired Computation and Applications Laboratory, USTC, China (2009)