Skip to main content

Mosquito Host-Seeking Algorithm Based on Random Walk and Game of Life

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10955))

Abstract

Mosquito Host-seeking Algorithm (MHSA) is a novel bionic algorithm. It simulates the behavior of mosquito seeking host. MHSA can find near-optimum solutions for the traveling salesman problem (TSP), however there are two drawbacks. First, it may be trapped into local optimum. Second, the solution exists several circles sometimes. In this paper, we adopt the Random Walk and the Game of Life strategies to improve MHSA, and propose a Random Walk and Game of Life Host-seeking Algorithm (RGHSA). RGHSA model is proposed to solve these two drawbacks. We use set theory and probability theory to prove the validity of the model. TSPlib is a benchmark for TSP. In the simulation, we choose server datasets from TSPlib, and compare the simulation result of RGHSA with original MHSA, Simulated Annealing Algorithm (SA) and Ant Colony Optimization Algorithm (ACO). The result shows that RGHSA have a good performance in TSP.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Hamerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 600–607. ACM (2002)

    Google Scholar 

  2. Matas, J., Kittler, J.: Spatial and feature space clustering: Applications in image analysis. In: Hlaváč, V., Šára, R. (eds.) CAIP 1995. LNCS, vol. 970, pp. 162–173. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60268-2_293

    Chapter  Google Scholar 

  3. Natali, A., Toschi, E., Baldeweg, S., et al.: Clustering of insulin resistance with vascular dysfunction and low-grade inflammation in type 2 diabetes. Diabetes 55(4), 1133–1140 (2006)

    Article  Google Scholar 

  4. Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. J. Comput. Biol. 6(3–4), 281–297 (1999)

    Article  Google Scholar 

  5. Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques In: KDD Workshop on Text Mining, vol. 400(1), pp. 525–526 (2000)

    Google Scholar 

  6. Hu, T., Liu, C., Tang, Y., et al.: High-dimensional clustering: a clique-based hypergraph partitioning framework. Knowl. Inf. Syst. 39(1), 61–88 (2014)

    Article  Google Scholar 

  7. Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: a review. Comput. Stat. Data Anal. 71, 52–78 (2014)

    Article  MathSciNet  Google Scholar 

  8. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  9. Zhu, X., Huang, Z., Yang, Y., et al.: Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recogn. 46(1), 215–229 (2013)

    Article  Google Scholar 

  10. Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1–14 (2013)

    Article  Google Scholar 

  11. Soltanolkotabi, M., Elhamifar, E., Candes, E.J.: Robust subspace clustering. Ann. Stat. 42(2), 669–699 (2014)

    Article  MathSciNet  Google Scholar 

  12. Bouveyron, C.: Model-based clustering of high-dimensional data in Astrophysics. EAS Publ. Ser. 77, 91–119 (2016)

    Article  Google Scholar 

  13. Han, E.H., Karypis, G., Kumar, V., et al.: Hypergraph based clustering in high-dimensional data sets: a summary of results. IEEE Data Eng. Bull. 21(1), 15–22 (1998)

    Google Scholar 

  14. Sun, L., Ji, S., Ye, J.: Hypergraph spectral learning for multi-label classification. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 668–676. ACM (2008)

    Google Scholar 

  15. Huang, Y., Liu, Q., Zhang, S., et al.: Image retrieval via probabilistic hypergraph ranking. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3376–3383. IEEE (2010)

    Google Scholar 

  16. Wang, M., Liu, X., Wu, X.: Visual classification by ℓ1-hypergraph modeling. IEEE Trans. Knowl. Data Eng. 27(9), 2564–2574 (2015)

    Article  Google Scholar 

  17. Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: Papers on Twenty-Five Years of Electronic Design Automation, pp. 241–247. ACM (1988)

    Google Scholar 

  18. Huang, D.J.H., Kahng, A.B.: When clusters meet partitions: new density-based methods for circuit decomposition. In: Proceedings of the 1995 European Conference on Design and Test. IEEE Computer Society (1995)

    Google Scholar 

  19. Karypis, G., Aggarwal, R., Kumar, V., et al.: Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Trans. Very Large Scale Integr. VLSI Syst. 7(1), 69–79 (1999)

    Article  Google Scholar 

  20. Cai, W., Young, E.F.Y.: A fast hypergraph bipartitioning algorithm. In: 2014 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 607–612. IEEE (2014)

    Google Scholar 

  21. Lotfifar, F., Johnson, M.: A Serial Multilevel Hypergraph Partitioning Algorithm. arXiv preprint arXiv:1601.01336 (2016)

  22. Henne, V., Meyerhenke, H., Sanders, P., et al.: n-Level Hypergraph Partitioning. arXiv preprint arXiv:1505.00693 (2015)

  23. Liu, H., Latecki, L.J., Yan, S.: Dense subgraph partition of positive hypergraphs. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 541–554 (2015)

    Article  Google Scholar 

  24. Jagannathan, J., Sherajdheen, A., Deepak, R.M.V., et al.: License plate character segmentation using horizontal and vertical projection with dynamic thresholding. In: 2013 International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), pp. 700–705. IEEE (2013)

    Google Scholar 

  25. Tuba, E., Bacanin, N.: An algorithm for handwritten digit recognition using projection histograms and SVM classifier. In: 2015 23rd Telecommunications Forum Telfor (TELFOR), pp. 464–467. IEEE (2015)

    Google Scholar 

  26. Hinton, G., Roweis, S.: Stochastic neighbor embedding. In: NIPS. 15, pp. 833–840 (2002)

    Google Scholar 

  27. Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  28. Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in sparse graphs in near-optimal time. In: Cheong, O., Chwa, K.-Y., Park, K. (eds.) ISAAC 2010. LNCS, vol. 6506, pp. 403–414. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17517-6_36

    Chapter  Google Scholar 

  29. The Semeion dataset. https://archive.ics.uci.edu/ml/datasets/Semeion+Handwritten+digit

  30. Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)

    Article  Google Scholar 

  31. The MNIST dataset. http://yann.lecun.com/exdb/mnist/index.html

  32. The USPS dataset. http://www.cs.nyu.edu/~roweis/data/html

  33. The Binaryalphadigs dataset. http://www.cs.toronto.edu/~roweis/data/binaryalphadigs.mat

  34. Van der Maaten, L.: A new benchmark dataset for handwritten character recognition, pp. 2–5. Tilburg Universit (2009)

    Google Scholar 

  35. Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. North-Holland, Amsterdam (1987)

    Google Scholar 

  36. Sun, X., Tian, S., Lu, Y.: High dimensional data clustering by partitioning the hypergraphs using dense subgraph partition. In: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015). International Society for Optics and Photonics (2015)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 61472293). Research Project of Hubei Provincial Department of Education (Grant No. 2016238).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiang Feng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhu, Y., Feng, X., Yu, H. (2018). Mosquito Host-Seeking Algorithm Based on Random Walk and Game of Life. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_78

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95933-7_78

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95932-0

  • Online ISBN: 978-3-319-95933-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics