Advertisement

Multi-objective Optimization of Construction Project Based on Multi Ant Colony Algorithm

  • Jieyun YangEmail author
Conference paper
  • 12 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)

Abstract

Modern construction projects involve many aspects, frequent safety accidents also show that the traditional project management model has been unable to adapt to large-scale complex construction projects. An advanced method is needed to deal with the growing construction projects. Based on this, this paper proposes a multi-objective optimization research of construction project based on multi ant colony algorithm. Taking the construction period, cost and quality of the construction project as the object of optimization, the pavement reconstruction project of Dongjiangyuan Avenue in Ganzhou City, Jiangxi Province is selected for demonstration. The results show that the multi-objective optimization method proposed in this paper accords with the reality. The total quality score of the optimized project is 79.6, the total cost is 4.05 million yuan, and the total construction period is 120 days.

Keywords

Construction project Multi-objective optimization Multi ant colony algorithm 

References

  1. 1.
    Banihashemi, S., Hosseini, M.R., Golizadeh, H., Sankaran, S.: Critical success factors (CSFs) for integration of sustainability into construction project management practices in developing countries. Int. J. Proj. Manag. 35(6), 1103–1119 (2017)CrossRefGoogle Scholar
  2. 2.
    Oke, A., Ogungbile, A., Oyewobi, L., Tengan, C.: Economic development as a function of construction project performance. J. Constr. Proj. Manag. Innov. 6(2), 1447–1459 (2016)Google Scholar
  3. 3.
    Akinbile, B.F., Ofuyatano, M., Oni, O.Z., Agboola, O.D.: Risk management and its influence on construction project in Nigeria. Ann. Fac. Eng. Hunedoara 16(3), 169–174 (2018)Google Scholar
  4. 4.
    Rajabioun, R.: Multi-objective optimization using Cuckoo optimization algorithm: a game theory approach. Int. J. Acad. Res. Comput. Eng. 1(2), 33–43 (2016)Google Scholar
  5. 5.
    Mirjalili, S., Jangir, P., Saremi, S.: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl. Intell. 46(1), 79–95 (2017)CrossRefGoogle Scholar
  6. 6.
    Mirjalili, S.Z., Mirjalili, S., Saremi, S., Faris, H., Aljarah, I.: Grasshopper optimization algorithm for multi-objective optimization problems. Appl. Intell. 48(4), 805–820 (2018)CrossRefGoogle Scholar
  7. 7.
    Zhang, Y., Gong, D.W., Cheng, J.: Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 14(1), 64–75 (2017)CrossRefGoogle Scholar
  8. 8.
    Liu, J., Yang, J., Liu, H., Tian, X., Gao, M.: An improved ant colony algorithm for robot path planning. Soft. Comput. 21(19), 5829–5839 (2017)CrossRefGoogle Scholar
  9. 9.
    Sun, Y., Dong, W., Chen, Y.: An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun. Lett. 21(6), 1317–1320 (2017)CrossRefGoogle Scholar
  10. 10.
    Qin, W., Zhang, J., Song, D.: An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. J. Intell. Manuf. 29(4), 891–904 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Liaoning Jianzhu Vocational CollegeLiaoyangChina

Personalised recommendations