Skip to main content
SpringerLink
Log in

Dynamic strategy based parallel ant colony optimization on GPUs for TSPs

  • Highlight
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Blum C, Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv, 2003, 35: 268–308

    Article  Google Scholar 

  2. Dorigo M, Stützle T. Ant Colony Optimization. Cambridge: MIT Press, 2004. 65–90

    MATH  Google Scholar 

  3. Alba E, Luque G, Nesmachnow S. Parallel metaheuristics: recent advances and new trends. Int Trans Oper Res, 2013, 20: 1–48

    Article  MATH  Google Scholar 

  4. Uchida A, Ito Y, Nakano K. An efficient GPU implementation of ant colony optimization for the traveling salesman problem. In: Proceedings of the 2012 3rd International Conference on Networking and Computing (ICNC), Okinawa, 2012. 94–102

    Chapter  Google Scholar 

  5. Cecilia J M, Garcia J M, Nisbet A, et al. Enhancing data parallelism for ant colony optimization on GPUs. J Parallel Distr Com, 2013, 73: 42–51

    Article  Google Scholar 

  6. Dawson L, Stewart I. Improving ant colony optimization performance on the GPU using CUDA. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, 2013. 1901–1908

    Chapter  Google Scholar 

  7. Zhou Y, He F Z, Qiu Y M. Optimization of parallel iterated local search algorithms on graphics processing unit. J Supercomput, 2016, 72: 2394–2416

    Article  Google Scholar 

  8. Wu Y Q, He F Z, Zhang D J, et al. Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans Serv Comput, 2016, doi: 10.1109/TSC.2015.2501981

    Google Scholar 

  9. Li K, He F Z, Chen X. Real time object tracking via compressive feature selection. Front Comput Sci-Chi, 2016, 10: 689–701

    Article  Google Scholar 

  10. Cheng Y, He F Z, Wu Y Q, et al. Meta-operation conflict resolution for human-human interaction in collaborative feature-based CAD systems. Cluster Comput, 2016, 19: 237–253

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by National Science Foundation of China (Grant Nos. 61472289, 61502353) and Hubei Province Science Foundation (Grant No. 2015CFB254). The authors thank Dr. Cecilia for providing the CUDA source code in [5], which is a great benchmark for comparison. Supporting information Appendixes A–C, including Algorithm B5, Tables C4 and C5. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Yi Zhou & Fazhi He

  2. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China

    Yi Zhou

  3. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Yimin Qiu

Authors
  1. Yi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fazhi He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yimin Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fazhi He.

Additional information

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., He, F. & Qiu, Y. Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Sci. China Inf. Sci. 60, 068102 (2017). https://doi.org/10.1007/s11432-015-0594-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-015-0594-2

Search

Navigation