Abstract
The simulated annealing algorithm (SAA) is a well-established approach to the approximate solution of combinatorial optimisation problems. SAA allows for occasional uphill moves in an attempt to reduce the probability of becoming stuck in a poor but locally optimal solution. Previous work showed that SAA can find better solutions, but it takes much longer time. In this paper, in order to harness the power of the very recent hybrid Many Integrated Core Architecture (MIC), we propose a new parallel simulated annealing algorithm customised for MIC. Our experiments with the Travelling Salesman Problem (TSP) show that our parallel SAA gains significant speedup.
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References
Bo, S., Yong, Z.G., Shao-hua, W., Xiao-wei, L., Qing, Z.: Research of offload parallel method based on MIC platform. Comput. Sci. 41(6), 477–480 (2014)
Choong, A., Beidas, R., Zhu, J.: Parallelizing simulated annealing-based placement using GPGPU. In: International Conference on Field Programmable Logic and Applications, FPL 2010, pp. 31–34. IEEE (2010)
Guo, S., Dou, Y., Lei, Y.: GPU parallel optimization of the oceanic general circulation model pop. Comput. Eng. Sci. 34(8), 147–153 (2012)
Hansen, P.B.: Studies in Computational Science: Parallel Programming Paradigms, 1st edn. Prentice Hall PTR, Upper Saddle River (1995)
Jie, F., Guohua, Z.: Parallel ant colony optimization algorithm with GPU-acceleration based on all-in-roulette selection. Comput. Digital Eng. 39(5), 23–26 (2011)
Johnson, D.S.: Local optimization and the traveling salesman problem. In: Paterson, M.S. (ed.) Automata, Languages and Programming. LNCS, vol. 443, pp. 446–461. Springer, Heidelberg (1990)
Johnson, D.S., Aragon, C.R., McGeoch, L.A., Schevon, C.: Optimization by simulated annealing: an experimental evaluation; part i, graph partitioning. Oper. Res. 37(6), 865–892 (1989)
Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Ma, J., Li, K.p., Zhang, L.Y.: The adaptive parallel simulated annealing algorithm based on TBB. In: 2nd International Conference on Advanced Computer Control, ICACC 2010, vol. 4, pp. 611–615. IEEE (2010)
Radenski, A.: Distributed simulated annealing with MapReduce. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 466–476. Springer, Heidelberg (2012)
Wang, H., Osen, O., Li, G., Li, W., Dai, H.N., Zeng, W.: Big data and industrial internet of things for the maritime industry in northwestern norway. In: IEEE Region 10 Conference, TENCON 2015 (2015)
Wei, W.: The research on parallel algorithm of simulation annealing. Comput. Knowl. Technol. 3(7), 1523–1524 (2008)
Acknowledgement
The work described in this paper was partially supported by Macao Science and Technology Development Fund under Grant No. 096/2013/A3 and the NSFC-Guangdong Joint Fund under Grant No. U1401251 and Guangdong Science and Technology Program under Grant No.2015B090923004.
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Zhou, J., Xiao, H., Wang, H., Dai, HN. (2016). Parallelizing Simulated Annealing Algorithm in Many Integrated Core Architecture. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9787. Springer, Cham. https://doi.org/10.1007/978-3-319-42108-7_18
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DOI: https://doi.org/10.1007/978-3-319-42108-7_18
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