A Parallel Bacterial Foraging Optimization Algorithm implementation on GPU
Subject Areas : electrical and computer engineeringKeywords: Swarm based algorithm parallel bacterial foraging optimization CUDA GPU,
Abstract :
Bacterial foraging algorithm is one of the population-based optimization algorithms that used for solving many search problems in various branches of sciences. One of the issues discussed today is parallel implementation of population-based optimization algorithms on Graphic Processor Units. Due to the low speed of bacterial foraging algorithm in the face of complex problem and also lack the ability to solve large-scale problems by this algorithm, Implementation on the graphics processor is a suitable solution to cover the weaknesses of this algorithm. In this paper, we proposed a parallel version of bacterial foraging algorithm which designed by CUDA and has ability to run on GPUs. The performance of this algorithm is evaluated by using a number of famous optimization problems in comparison with the standard bacterial foraging optimization algorithm. The results show that Parallel Algorithm is faster and more efficient than standard bacterial foraging optimization algorithm.
[1] L. M. Schmitt, "Theory of genetic algorithms," Theoretical Computer Science Science Direct, vol. 259, no. 1-2, pp. 1-61, 2001.
[2] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942-1948, 27 Nov.- 1 Dec. 1995.
[3] A. Colorni, M. Dorigo, and V. Maniezzo, "Distributed optimization by ant colonies," in Proc. European Conf. on Artificial Life Paris France, vol. 142, pp. 134-142, 1991.
[4] T. Sato and M. Hagiwara, "Bee system: finding solution by a concentrated search," in Proc. IEEE Int. Conf. on Systems Man and Cybernetics Computational Cybernetics and Simulation, vol. 4, pp. 3954-3959, 12-15 Oct. 1997.
[5] P. Pospichal, J. Jaros, and J. Schwarz, "Parallel genetic algorithm on the CUDA architecture," EvoApplications, vol. 6024, no. 1, pp. 442-451, 2010.
[6] G. H. Luo, S. K. Huang, Y. S. Chang, and S. M. Yuan, "A parallel bee algorithm implementation on GPU," J. of System Architecture, vol. 60, no. 3, pp. 271-279, Mar. 2013.
[7] Y. Zhou and Y. Tan, "GPU-based parallel particle swarm optimization," in Proc. IEEE Congress on Evolutionary Computation CEC'09, pp. 1493-1500, 18-21 May 2009.
[8] A. Delevacq, P. Delisle, M. Gravel, and M. Krajecki, "Parallel ant colony optimization on graphics processing units," J. of Parallel and Distributed Computing), vol. 73, no. 1, pp. 52-61, Jan. 2013.
[9] K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control," IEEE Control System Magazine, vol. 22, no. 3, pp. 52-67, Jun. 2002.
[10] D. H. Kim, A. Abraham, and J. H. Cho, "A hybrid genetic algorithm and bacterial foraging approach for global optimization," Information Science, vol. 177, no. 18, pp. 3918-3937, 15 Sept. 2007.
[11] S. Mishra, "A hybrid least square-fuzzy bactrial foraging strategy for harmonic estimation," IEEE Trans. on Evolutionary Computation, vol. 9, no. 1, pp. 61-73, Feb. 2005.
[12] C. Cabrita, J. Botzheim, A. E. Ruano, and L. T. Koczy, "Genetic programing and bacterial algorithm for neural networks and fuzzy systems design," in Proc. Conf. on Intelligent Control Systems and Signal Processing, 6 pp., Faro, Portugal, 2003.
[13] A. Biswas, S. Dasgupta, S. Das, and A. Abraham, "Synergy of PSO and bacterial foraging optimization-a comparative study on numerical benchmarks," Innovations in Hybrid Intelligent Systems, vol. 44, no. 1, pp. 255-263, 2007.
[14] H. Chen, Y. Zhu, and K. Hu, "Adaptive bacterial foraging optimization," Abstract and Applied Analysis, vol. 2011, Article ID 108269, 27 pp., 2011.
[15] K. M. Bakwad, S. S. Pattnik, B. S. Shi, S. Devi, and M. R. Lohakare, "Parallel bacterial foraging optimization for video compression," International J. of Recent Trends in Engineering, vol. 1, no. 1, pp. 118-122, May 2009.
[16] S. I. Bejinariu, "Image registration using bacterial foraging optimization algorithm on multi-core processors," in Proc. 4th Int. Symp. on Electrical and Electronics Engineering, ISEEE'13, 6 pp., 11-13 Oct. 2013.
[17] M. Molga and C. Smutnicki, "Test functions for optimization needs," J. of Computer Sciences and Applications Science and Education Publishing, vol. 2, no. 2, pp. 23-30, 2005.
[18] H. Chen, Y. Zhu, and K. Hu, "Cooperative bacterial foraging optimization," Discrete Dynamics in Nature and Society, vol. 2009, Article ID 815247, 17 pp., 2009.