Adaptive Neighborhood Search’s DGSO Applied to Travelling Saleman Problem

  • Wenbo Dong
  • Kang ZhouEmail author
  • Qinhong Fu
  • Yingying Duan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 562)


In order to further study the effectiveness and applicability of the glowworm swarm optimization algorithm, this paper proposed a discrete glowworm swarm optimization algorithm with an adaptive neighborhood search, and used it to solve traveling salesman problem (TSP). Based on the analysis and optimization of the different genetic operations, a new adaptive DGSO algorithm is presented (ADGSO), which is effective for both local search and global search. And we defined a new kind of glowworm, which can adjust the flight length of particles by self-adapting. By solving the different instances of TSP, experimental results indicate that ADGSO has a remarkable quality of the global convergence reliability and convergence velocity. It solved the problems of traditional DGSO algorithm “premature”. Unlike existing TSP approaches that often aggregate multiple criteria and constraints into a compromise function, the proposed ADGSO optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace. The ADGSO is applied to solve the TSP, which yields solutions better than or competitive as compared to the best solutions published in literature.


ADGSO Local optimization operator Adaptive TSP 



This project was supported by National Natural Science Foundation of China (Grant No. 61179032), and the Graduate Innovation Fund of Wuhan Polytechnic University (2014cx007). In addition, we would also thank every authors appeared in the references.


  1. 1.
    Zhou, K., Chen, J.: Simulation DNA algorithm of set covering problem. Appl. Math. Inf. Sci. 8, 139–144 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Song, T., Pan, L., Wang, J., et al.: Normal forms of spiking neural P systems with anti-spikes. IEEE Trans. Nanobiosci. 11(4), 352–359 (2012)CrossRefGoogle Scholar
  3. 3.
    Zhou, K., Fan, L.L., Shao, K.: Simulation DNA algorithm model of satisfiability problem. J. Comput. Theor. Nanosci. 12, 1220–1227 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhou, K., Tong, X.J., Xu, J.: Closed circle DNA algorithm of change positive-weighted hamilton circuit problem. J. Syst. Eng. Electron. 20, 636–642 (2009)Google Scholar
  5. 5.
    Song, T., Pan, L., Păun, G.: Asynchronous spiking neural P systems with local synchronization. Inf. Sci. 219, 197–207 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Zhou, Y.Q., Huang, Z.X., Liu, H.X.: Discrete glowworm swarm optimization algorithm for TSP problems. Acta Electronica Sin. 40, 1164–1170 (2012)Google Scholar
  7. 7.
    Krishnand, K.N., Ghose, D.: Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int. J. Comput. Intell. Stud. 1, 93–119 (2009)CrossRefGoogle Scholar
  8. 8.
    Li, H.Z., Yang, J.H.: Application in TSP based on genetic algorithm. Comput. Knowl. Technol. 6(3), 672–673 (2010)MathSciNetGoogle Scholar
  9. 9.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  10. 10.
    Song, T., Pan, L., Jiang, K., et al.: Normal forms for some classes of sequential spiking neural P systems. IEEE Trans. Nanobiosci. 12(3), 255–264 (2013)CrossRefGoogle Scholar
  11. 11.
    Yao, M.H., Wang, N., Zhao, L.P.: Improved simulated annealing algorithm and genetic algorithm for TSP. Comput. Eng. Appl. 49(14), 60–65 (2013)Google Scholar
  12. 12.
    Zhong, Y.W., Yang, J.G., Ning, Z.Y.: Discrete particle swarm optimization algorithm for TSP problem. Syst. Eng. Theory Pract. 26(6), 88–94 (2006)Google Scholar
  13. 13.
    He, Y., Liu, G.Y.: Research on solving TSP in tabu search algorithm. J. Southwest China Normal Univ. 27(3), 341–345 (2002)Google Scholar
  14. 14.
  15. 15.
    Song, T., Pan, L.: Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy. IEEE Trans. Nanobiosci. 14(1), 38–44 (2015)CrossRefGoogle Scholar
  16. 16.
    Song, T., Pan, L.: Spiking neural P systems with rules on synapses working in maximum spiking strateg. IEEE Trans. Nanobiosci. 14(4), 465–477 (2015)CrossRefGoogle Scholar
  17. 17.
    Zhang, X., Pan, L., Paun, A.: On the universality of axon P systems. IEEE Trans. Neural Networks Learn. Syst. (2015). doi: 10.1109/TNNLS.2015.2396940
  18. 18.
    Shi, X., Wang, Z., Deng, C., Song, T., Pan, L., Chen, Z.: A novel bio-sensor based on DNA strand displacement. Plos One 9, e108856 (2014)CrossRefGoogle Scholar
  19. 19.
    Wang, X., Miao, Y., Cheng, M.: Finding motifs in DNA sequences using low-dispersion sequences. J. Comput. Biol. 21(4), 320–329 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang, X., Miao, Y.: GAEM: a hybrid algorithm incorporating GA with EM for planted edited motif finding problem. Curr. Bioinform. 9(5), 463–469 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Wenbo Dong
    • 1
  • Kang Zhou
    • 1
    Email author
  • Qinhong Fu
    • 1
  • Yingying Duan
    • 1
  1. 1.School of Math and ComputerWuhan Polytechnic UniversityWuhanChina

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