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Research on Swarm Intelligence Algorithm Based on Prefabricated Construction Vehicle Routing Problem

  • Xingrui Chen
  • Jun-Qing Li
  • Yongqin Jiang
  • Yunqi Han
  • Kun Jiang
  • Xiaoping Lin
  • Pei-Yong Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Prefabricated buildings are becoming increasingly popular in China. Logistics distribution is an important aspect of their deployment. At present, there are few logistics management issues, and the logistics and distribution problems are gradually increasing. The planning of path problems is also one of the issues that many scholars are concerned about. With the attention of many experts, a single intelligent optimization algorithm fails to achieve the optimal path and does not apply to large-scale and complicated path planning. Of the existing swarm intelligence algorithms, the ant colony algorithm is the most widely studied one, whereas other swarm intelligence algorithms or hybrid swarm algorithms are relatively less studied. This study combines the research of swarm intelligence algorithms at home and abroad, and thus presents a comprehensive review and analysis of the swarm intelligence algorithms proposed by scholars, which is of significant theoretical importance for the solution of realistic path optimization problems.

Keywords

Assembly building Swarm intelligence Ant colony algorithm Logistics and distribution 

Notes

Acknowledgement

This research is partially supported by the National Science Foundation of China (61773192, 61503170, 61603169, 61773246), Shandong Province Higher Educational Science and Technology Program (J17KZ005, J14LN28), Natural Science Foundation of Shandong Province (ZR2016FL13, ZR2017BF039), Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education (K93-9-2017-02), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).

References

  1. 1.
    Fisher, M.L.: Vehicle routing problem. Oper. Res. Manag. Sci. 8, P1–P33 (1995)Google Scholar
  2. 2.
    Holland, J.H.: Outline for a logical theory of adaptive systems. J. Assoc. Comput. Mach. 9(3), 297–314 (1962)CrossRefGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufman Publisher, San Francisco (2001)Google Scholar
  4. 4.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  5. 5.
    Liu, C.A., Yan, X.H., Liu, C.Y., et al.: The wolf colony algorithm and applications. Chin. J. Electron. 20(2), 212–216 (2011)Google Scholar
  6. 6.
    Tsai, P.W., Pan, J.S., Chen, S.M., et al.: Parallel cat swarm optimization. In: International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3328–3333. IEEE (2008)Google Scholar
  7. 7.
    Santosa, B., Ningrum, M.K.: Cat swarm optimization for clustering. In: International Conference of Soft Computing and Pattern Recognition, SOCPAR 2009, pp. 54–59. IEEE (2009)Google Scholar
  8. 8.
    Chittineni, S., Abhilash, K., Mounica, V., et al.: Cat swarm optimization based neural network and particle swarm optimization based neural network in stock rates prediction. In: Proceedings of the 3rd International Conferences on Machine Learning and Computing, pp. 292–296 (2011)Google Scholar
  9. 9.
    Ganapati, P., Pyari, M.P., Babita, M.H.: System identification using cat swarm optimization. Expert Syst. Appl. 38(10), 12671–12683 (2011)CrossRefGoogle Scholar
  10. 10.
    Carmelo, J.A., Filho, B., Fernando, B., Lins, J.C.C.: A novel search algorithm based on fish school behavior. In: IEEE International Conference on Systems, pp. 2645–2651 (2008) Google Scholar
  11. 11.
    Ayed, S., Imtiaz, S., Sabah, A.M.: Particle swarm optimization for task assignment problem. Microprocess. Mincrosyst. 26, 363–371 (2002)CrossRefGoogle Scholar
  12. 12.
    Hoffman, K.L., Padberg, M., Rinaldi, G.: Traveling salesman problem. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Operations Research and Management Science. Springer, Boston (2013)Google Scholar
  13. 13.
    Fisher, M.L.: Vehicle routing problem. Oper. Res. Manag. Sci. 8, 1–3 (1995)zbMATHGoogle Scholar
  14. 14.
    Liu, R., Jiang, Z., Geng, N.: A hybrid genetic algorithm for the multi-depot open vehicle routing problem. OR Spectr. 36(2), 401–421 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zou, T., Li, N., Sun, D.: Genetic algorithm for multiple-depot vehicle routing problem. Comput. Eng. Appl. 40(21), 82–83 (2004)Google Scholar
  16. 16.
    Korayem, L., Khorsid, M., Kassem, S.S.: Using grey wolf algorithm to solve the capacitated vehicle routing problem. In: IOP Conference Series Materials Science and Engineering, May 2015CrossRefGoogle Scholar
  17. 17.
    Zhi, Y., Ye, C.: Hierarchical algorithm model for vehicle delivery scheduling problem in multiple distribution centers. J. Syst. Manag. 23(4), 602–606 (2014)MathSciNetGoogle Scholar
  18. 18.
    Wu, H., Zhang, F.: A uncultivated wolf pack algorithm for high-dimensional functions and its application in parameters optimization of PID controller. In: IEEE Congress on Evolutionary Computation, pp. 1477–1482. IEEE (2014)Google Scholar
  19. 19.
    Li, X.L., Lu, F.: Applications of artificial fish school algorithm in combinatorial optimization problems (2004)Google Scholar
  20. 20.
    Fang, J., Zhang, Q.: Distribution center decision-making problem and fish school algorithm. Comput. Appl. 34(5), 1652–1655 (2011)Google Scholar
  21. 21.
    He, S., Belacel, N., Hamam, H., Bouslimani, Y.: Fuzzy clustering with improved artificial fish swarm algorithm. Comput. Sci. Optim. (CSO) 2(1), 317–321 (2009)Google Scholar
  22. 22.
    Li, X., Lu, F., Tian, G.: Application of artificial fish swarm algorithm for combinatorial optimization. J. Shandong Univ. Eng. Edn. 34(5), 64–67 (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xingrui Chen
    • 1
  • Jun-Qing Li
    • 1
    • 2
    • 3
    • 4
  • Yongqin Jiang
    • 3
  • Yunqi Han
    • 3
  • Kun Jiang
    • 3
  • Xiaoping Lin
    • 3
  • Pei-Yong Duan
    • 2
  1. 1.School of InformationShandong Normal UniversityJinanChina
  2. 2.School of ComputerLiaocheng UniversityLiaochengChina
  3. 3.China Key Laboratory of Computer Network and Information IntegrationSoutheast University, Ministry of EducationNanjingPeople’s Republic of China
  4. 4.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

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