Parallelizing Simulated Annealing Algorithm in Many Integrated Core Architecture

  • Junhao Zhou
  • Hong Xiao
  • Hao WangEmail author
  • Hong-Ning Dai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9787)


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.


Parallel computing Simulated annealing MIC optimization 



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.


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Guo, S., Dou, Y., Lei, Y.: GPU parallel optimization of the oceanic general circulation model pop. Comput. Eng. Sci. 34(8), 147–153 (2012)Google Scholar
  4. 4.
    Hansen, P.B.: Studies in Computational Science: Parallel Programming Paradigms, 1st edn. Prentice Hall PTR, Upper Saddle River (1995)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Wei, W.: The research on parallel algorithm of simulation annealing. Comput. Knowl. Technol. 3(7), 1523–1524 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Junhao Zhou
    • 1
  • Hong Xiao
    • 1
  • Hao Wang
    • 2
    Email author
  • Hong-Ning Dai
    • 3
  1. 1.Faculty of Computer, Guangdong University of TechnologyGuangzhouChina
  2. 2.Big Data Lab, Faculty of Engineering and Natural SciencesNorwegian University of Science and TechnologyÅlesundNorway
  3. 3.Faculty of Information TechnologyMacau University of Science and TechnologyMacauChina

Personalised recommendations