Abstract
In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimization, Differential Evolution, and Scatter Search. A GPU-based implementation, obviously, does not change the general properties of the algorithms. As well, we give for granted that GPU-based implementation of both algorithm and fitness function produces a significant speed-up with respect to a sequential implementation. Accordingly, the main goal of this work has been to fairly assess the efficiency of the GPU-based implementations of the three metaheuristics, based on the statistical analysis of the results they obtain in optimizing a benchmark of twenty functions within a prefixed limited time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford (1999)
Das, S., Suganthan, P.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)
de Veronese, L., Krohling, R.: Swarm’s flight: Accelerating the particles using C-CUDA. In: Proc. IEEE Congress on Evolutionary Computation, pp. 3264–3270 (2009)
de Veronese, L., Krohling, R.: Differential evolution algorithm on the GPU with C-CUDA. In: Proc. IEEE Congress on Evolutionary Computation, pp. 1–7 (2010)
Duarte, A., Martí, R., Glover, F., Gortázar, F.: Hybrid scatter tabu search for unconstrained global optimization. Annals of Operations Research 183(1), 95–123 (2011)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2003)
Glover, F.: Heuristics for integer programming using surrogate constraints. Decision Sciences 8(1), 156–166 (1977)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Krömer, P., Snåšel, V., Platoš, J., Abraham, A.: Many-threaded implementation of differential evolution for the CUDA platform. In: Proc. 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1595–1602. ACM (2011)
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium (2011)
Mussi, L., Daolio, F., Cagnoni, S.: Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. Information Sciences 181(20), 4642–4657 (2011)
Mussi, L., Nashed, Y.S.G., Cagnoni, S.: GPU-based asynchronous particle swarm optimization. In: Proc. 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1555–1562. ACM (2011)
Nashed, Y.S.G., Ugolotti, R., Mesejo, P., Cagnoni, S.: libCudaOptimize: an Open Source Library of GPU-based Metaheuristics. In: Proc. Genetic and Evolutionary Computation Conference, GECCO 2012 (in press, 2012)
nVIDIA Corporation: nVIDIA CUDA Programming Guide v. 4.0. (2011)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intelligence 1(1), 33–57 (2007)
Storn, R., Price, K.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute (1995)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Natural Computing, 1–50 (2005)
Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proc. IEEE Congress on Evolutionary Computation, pp. 1980–1987 (2004)
Wets, F.J., Solis, R.J.: Minimization by random search techniques. Mathematics of Operations Research 6(1), 19–30 (1981)
Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: Proc. IEEE Congress on Evolutionary Computation, pp. 1493–1500 (2009)
Zhu, W.: Massively parallel differential evolution–pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems. Journal of Global Optimization 50(3), 417–437 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nashed, Y.S.G., Mesejo, P., Ugolotti, R., Dubois-Lacoste, J., Cagnoni, S. (2012). A Comparative Study of Three GPU-Based Metaheuristics. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-32964-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32963-0
Online ISBN: 978-3-642-32964-7
eBook Packages: Computer ScienceComputer Science (R0)