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Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)


Digital Signal Processing and Artificial Intelligence for Automatic Learning


A Parallel Version of the JADE Algorithm using GPUS
Adriana Mexicano, Jesus C. Carmona, Nelva N. Almaza, Lilia Garcia, Francisco Arguelles
Division de Estudios de Posgrado e Investigacion, Tecnologico Nacional de Mexico, Tamaulipas., Division de Estudios de Posgrado e Investigacion, Tecnologico Nacional de Mex
Abstract: This work presents a parallel implementation of JADE: Adaptive Differential Evolution With Optional External Archive, using the Compute Unified Device Architecture (CUDA), in order to reduce the execution run-time of the algorithm. The algorithm was tested using the well-known function Sphere and the execution run time was compared against its sequential version. The results were measured in terms of “Speed-up” and they show that the execution run-time can be reduced significantly by the use of CUDA, this benefit can be observed better when working with large amounts of data. However, not necessarily the population with more data reaches the best performance.
Keywords: Optimization, JADE, CUDA, Parallelization A Parallel Version of the JADE Algorithm using GPUS
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[1] Arellano, J., Guzman, A., Godoy, S., and Barron, R. (2016). Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: Dela. Soft Computing 20 (3) 895–905.
[2] Conte, D., Dambrosio, R., and Paternoster, B. (2016). Gpu-acceleration of waveform relaxation methods for large differential systems. Numerical Algorithms, 71 (2) 293–310.
[3] Davendra, D., Gaura, J., Bialic-Davendra, M., Senkerik, R. (2012). Cuda based enhanced differential evolution: A computational analysis. In: Proceedings 26th European Conference on Modelling and Simulation. Univerzita Tomase Bati ve Zline, 399– 404.
[4] De Veronese, L., Krohling, R. A. (2010). Differential evolution algorithm on the gpu with c-cuda. In: Proceedings of the IEEE Congress on Evolutionary Computation CEC (2010), IEEE Xplorer, 1–7.
[5] Hu, D., Harding, S., Banzhaf, W. Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm. Genetic Programming and Evolvable Machines 11 (2) 205–225.
[6] Jimenez, C. O. F. A gpu-based parallel object kinetic monte carlo algorithm for the evolution of defects in irradiated materials. Computational Materials Science 113 (4) 178–186.
[7] Kr Nomer, P., Platos, J., Sn Lasel, V., and Abraham, A. Many-threaded differential evolution on the gpu, natural computing series. In Massively Parallel Evolutionary Computation on GPGPUs (2013), Springer-Verlag, 1–7.
[8] Lee, C., Yao, X. (2004). Evolutionary programming using mutations based on the levy probability distribution. Evolutionary Computation, 8 (1) 1–13.
[9] Lee, K. Y., El-Sharkawi, M. A (2008). Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems. Wiley-IEEE Press, New Jersey.
[10] NVIDIA. Nvidia cuda c++ programming guide, 2021.
[11] Price, K. V., Storn, R. M., Lampinen, J. A. (2005). Differential Evolution: A Practical Approach to Global Optimization. Springer-Verlag, New York.
[12] Qin, A., Raimondo, F., Forbes, F., Ong, Y. (2012). An improved cuda-based implementation ofdifferential evolution on gpu. In Proceedings of the 14th annual conference on Genetic and evolutionary computation (2012). Association for Computing Machinery, 991–998.
[13] Regulwar, D., Choudhari, S., Raj, P. (2010). Differential evolution algorithm with application to optimal operation of multipurpose reservoir. Journal of Water Resource and Protection, 2 (6) 560–568.
[14] Rivera, I., Vargas-Lombardo, M. (2012). Principios y campos de aplicaci Lon en cuda programaci Lon paralela y sus potencialidades. Nexo Revista Cientifica, 25 (2) 39–46.
[15] Sanchez, J., Galan, M., Rubio, E. (2004). Genetic algorithms and cellular automata: A new architecture for traffic light cycles. In Proceedings of the 2004 Congress on Evolutionary Computation, IEEE Xplorer, 1668–1674.
[16] Sanderson, A. C., Zhang, J. (2009). Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization. Springer Publishing Company, New York, 2009. Evolutionary Computation, 13 (5) 945–958.
[17] Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Ping Chen, Y., Auger, A., and Tiwari, S. (2005). Problem definitions and evaluation criteria for the cec 2005, special session on realparameter optimization.
[18] Zhang, J., Sanderson, A. C. (2009). Jade: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13 (5) 945–958.

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