Abstract
A GPU based enhanced differential evolution algorithm is presented in this chapter to solve the flow shop scheduling problem. The main premise is to show the effectiveness of using mainstream GPU hardware compared to high-end CPU, and analyze as to under what conditions it becomes viable. Both CUDA and OpenCL architecture is utilized and a comparison is done on the Mac OS X platform. The results validate that with increasing problem complexity, GPU programming becomes more viable.
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Chen, S., Davis, S., Jiang, H., Novobilski, A.: CUDA-Based Genetic Algorithm on Traveling Salesman Problem. In: Lee, R. (ed.) Computer and Information Science 2011. SCI, vol. 364, pp. 241–252. Springer, Heidelberg (2011)
CUDA webpage (2011), http://www.nvidia.com/object/cuda_-home_-new.html
Czapinski, M., Barnes, S.: Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform. Journal of Parallel and Distributed Computing 71, 802–811 (2011)
Davendra D.: Differential evolution algorithm for flow shop scheduling. Master’s thesis, University of the South Pacific, (2001).
Davendra, D., Onwubolu, G.: Enhanced differential evolution hybrid scatter search for discrete optimisation. In: Proc. of the IEEE Congress on Evolutionary Computation, Singapore, pp. 1156–1162 (September 2007)
Davendra, D., Onwubolu, G.: Flow shop scheduling using enhanced differential evolution. In: Proc. 21 European Conference on Modeling and Simulation, Prague, Czech Rep., pp. 259–264 (June 2007)
Hoffmann, J., El-Laithy, K., Güttler, F., Bogdan, M.: Simulating Biological-Inspired Spiking Neural Networks with OpenCL. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6352, pp. 184–187. Springer, Heidelberg (2010)
Kirk, D., Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann (2010)
Munshi, A., Gaster, B., Mattson, T., Fung, J., Ginsburg, D.: OpenCL Programming Guide. Addison-Wesley Professional (2010)
Mussi, L., Daolio, F., Cagnoni, S.: Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture. Information Sciences 181, 4642–4657 (2011)
Onwubolu, G., Davendra, D.: Scheduling flow shops using differential evolution algorithm. European Journal of Operations Research 171, 674–679 (2006)
Onwubolu, G., Davendra, D.: Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization. Springer, Germany (2009)
Onwubolu, G., Davendra, D.: Differential evolution for permutation- based combinatorial problems. In: Onwubolu, G., Davendra, D. (eds.) Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization. Springer, Germany (2009)
Onwubolu, G., Davendra, D.: Motivation for differential evolution for permutative - based combinatorial problems. In: Onwubolu, G., Davendra, D. (eds.) Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization. Springer, Germany (2009)
Onwubolu, G.: Optimization using differential evolution. Technical Report TR-2001-05, IAS, USP, Fiji (October 2005)
Onwubolu, G.: Emerging Optimization Techniques in Production Planning and Control. Imperial Collage Press, London (2002)
OpenCL webpage (2011) http://www.khronos.org/opencl/
Pinedo, M.: Scheduling: theory, algorithms and systems. Prentice Hall, Inc., New Jersey (1995)
Pospichal, P., Jaros, J., Schwarz, J.: Parallel Genetic Algorithm on the CUDA Architecture. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 442–451. Springer, Heidelberg (2010)
Price, K.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. McGraw Hill, International, UK (1999)
Robilliard, D., Marion-Poty, V., Fonlupt, C.: Genetic programming on graphics processing units. Genetic Programming and Evolvable Machines 10(4), 447–471 (2009)
Sanders, S., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional (2010)
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Davendra, D.D., Zelinka, I. (2013). GPU Based Enhanced Differential Evolution Algorithm: A Comparison between CUDA and OpenCL. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_33
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DOI: https://doi.org/10.1007/978-3-642-30504-7_33
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