Particle Swarm Optimization Algorithm Based on Graph Knowledge Transfer for Geometric Constraint Solving

  • Mingyu Sun
  • Qingliang LiEmail author
  • Jinlong Zhu
  • Yu Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


In order to more effectively solve complicated geometric constraint problems by applying swarm intelligence technologies, a particle swarm optimization (PSO) algorithm based on graph knowledge transfer for geometric constraint solving (GCS) is proposed. By fusing the graph knowledge transfer mechanism into the PSO algorithm to select parameters deciding the algorithm performance, avoiding getting stuck in a local extreme value and then making the algorithm stagnating when solving a practical complicated geometric constraint problem. Empirical results show that using the graph knowledge transfer mechanism to select the parameters of PSO can obtain high-quality parameters of GCS. It improves the efficiency and reliability of GCS and possess better convergence property.


Geometric constraint solving Particle swarm optimization Graph knowledge transfer 



This work is supported by National Natural Science Foundation of China (Grant No. 61604019), Science and Technology Development Project of Jilin Province (20160520098JH, 20180201086SF), Education Department of Jilin Province, (JJKH2018118KJ, JJKH20181165KJ), Talent Introduction Scientific Research Project of Changchun Normal University, China (RC2016009).


  1. 1.
    Cao, C.H., Zhang, Y.J., Li, W.H.: The application of crossbreeding particle swarm optimizer in the engineering geometric constraint solving. Chin. J. Sci. Instrum. 25, 397–400 (2004)Google Scholar
  2. 2.
    Cao, C.H., Tang, C., Zhao, D.Z., Zhang, B.: Application of the quantum particle swarm optimization approach in the geometric constraint problems. J. Northeast. Univ. (Nat. Sci.) 32(9), 1229–1232 (2011)Google Scholar
  3. 3.
    Cao, C.H., Wang, L.M., Zhao, D.Z., Zhang, B.: Geometric constraint solving based on niche improved particle swarm optimization 33(9), 2125–2129 (2012)Google Scholar
  4. 4.
    Yuan, H., Li, W.H., Li, N.: Tabu-PSO algorithm for solving geometric constraint problem. Microelectron. Comput. 27, 26–29 (2010)Google Scholar
  5. 5.
    Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)CrossRefGoogle Scholar
  6. 6.
    Chi, Y.H., Sun, F.C., Wang, W.J., Yu, C.M.: An improved particle swarm optimization algorithm with search space zoomed factor and attractor. Chin. J. Comput. 34(1), 115–130 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhou, X.Y., Wu, Z.J., Wang, H., Li, K.S., Zhang, H.Y.: Elite opposition-based particle swarm optimization. Acta Electronica Sin. 41(8), 1647–1652 (2013)Google Scholar
  8. 8.
    Tan, Y., Tan, G., Deng, S.: Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems. J. Central South Univ. 21(7), 2731–2742 (2014)CrossRefGoogle Scholar
  9. 9.
    Shen, Y.X., Zeng, C.H., Wang, X.F., Wang, X.Y.: A parallel-cooperative bare-bone particle swarm optimization algorithm. Acta Electronica Sin. 44(7), 1643–1648 (2016)Google Scholar
  10. 10.
    Zhao, J., Fu, Y., Mei, J.: An improved cooperative QPSO algorithm with adaptive mutation based on entire search history. Acta Electronica Sin. 44(12), 2901–2907 (2016)Google Scholar
  11. 11.
    Zhou, L.Y., Ding, L.X., Peng, H., Qiang, X.L.: Neighborhood centroid opposition-based particle swarm optimization. Acta Electronica Sin. 45(1), 2815–2824 (2017)Google Scholar
  12. 12.
    Lv, B.Q., Zhang, J.J., Li, Z.P., Liu, T.Z.: Fuzzy particlel swarm optimization based on filled function and transformation function. Acta Automatica Sin. 44(1), 74–86 (2018)zbMATHGoogle Scholar
  13. 13.
    Lee, S., Park, H., Jeon, M.: Binary particle swarm optimization with bit change mutation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E90-A(10), 2253–2256 (2007)CrossRefGoogle Scholar
  14. 14.
    Shi, Y. H., Russell, E.: Modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. IEEE, Piscataway (1998)Google Scholar
  15. 15.
    Erie, E., Marie, D. J., Terran, L.: Modeling transfer relationships between learning tasks for improved inductive transfer. In: Lectures Notes in Artificial Intelligence, vol. 5211, pp. 317–332 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mingyu Sun
    • 1
  • Qingliang Li
    • 1
    Email author
  • Jinlong Zhu
    • 1
  • Yu Zhang
    • 1
  1. 1.College of Computer Science and TechnologyChangchun Normal UniversityChangchunChina

Personalised recommendations