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

  • Yunxin Zhu
  • Xiang FengEmail author
  • Huiqun Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


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.


Mosquito host-seeking algorithm Traveling salesman problem Random walk Game of life 



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).


  1. 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. 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). Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. J. Comput. Biol. 6(3–4), 281–297 (1999)CrossRefGoogle Scholar
  5. 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. 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)CrossRefGoogle Scholar
  7. 7.
    Bouveyron, C., Brunet-Saumard, C.: Model-based clustering of high-dimensional data: a review. Comput. Stat. Data Anal. 71, 52–78 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 11.
    Soltanolkotabi, M., Elhamifar, E., Candes, E.J.: Robust subspace clustering. Ann. Stat. 42(2), 669–699 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bouveyron, C.: Model-based clustering of high-dimensional data in Astrophysics. EAS Publ. Ser. 77, 91–119 (2016)CrossRefGoogle Scholar
  13. 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. 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. 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. 16.
    Wang, M., Liu, X., Wu, X.: Visual classification by ℓ1-hypergraph modeling. IEEE Trans. Knowl. Data Eng. 27(9), 2564–2574 (2015)CrossRefGoogle Scholar
  17. 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. 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. 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)CrossRefGoogle Scholar
  20. 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. 21.
    Lotfifar, F., Johnson, M.: A Serial Multilevel Hypergraph Partitioning Algorithm. arXiv preprint arXiv:1601.01336 (2016)
  22. 22.
    Henne, V., Meyerhenke, H., Sanders, P., et al.: n-Level Hypergraph Partitioning. arXiv preprint arXiv:1505.00693 (2015)
  23. 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)CrossRefGoogle Scholar
  24. 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. 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. 26.
    Hinton, G., Roweis, S.: Stochastic neighbor embedding. In: NIPS. 15, pp. 833–840 (2002)Google Scholar
  27. 27.
    Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  28. 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). Scholar
  29. 29.
  30. 30.
    Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)CrossRefGoogle Scholar
  31. 31.
  32. 32.
  33. 33.
  34. 34.
    Van der Maaten, L.: A new benchmark dataset for handwritten character recognition, pp. 2–5. Tilburg Universit (2009)Google Scholar
  35. 35.
    Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. North-Holland, Amsterdam (1987)Google Scholar
  36. 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

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China University of Science and TechnologyShanghaiChina
  2. 2.Smart City Collaborative Innovation CenterShanghai Jiao Tong UniversityShanghaiChina

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