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Tytuł artykułu

Optimal allocation of urban land space based on NSGA2

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Urban land spatial optimization is one of the important issues in urban planning and land resource management. As the speed advancement of urbanization and the continuous increase of population, the rational use of land resources has become the key to sustainable urban development. Based on this, the study adopts the optimization goals of maximizing gross domestic product (GDP), reducing aerosol optical thickness and non-point source pollution (NPSP) load, and reducing land use change costs and incongruity. Three constraints are set simultaneously, including minimum construction land, water body, and cultivated land area. In addition, a fast non dominated sorting genetic algorithm (NSGA2) with elite strategy is used to address it. The outcomes denoted that the iterative distance of the proposed algorithm on the Bin and Cohen functions was only 0.048%, which was 0.522% lower than that of the NSGA2. Meanwhile, the reverse iteration distance value of this algorithm was only 4.14%, which was 22.76% lower than the adaptive weighted genetic algorithm. In addition, the algorithm’s Spacing value was only 4.28%, and the hypervolume index value was as high as 78.66%. This indicated that the research method had a good optimization effect on the optimal allocation (OA) of land space in urban agglomerations, providing scientific decision-making support for sustainable urban development.
Rocznik
Strony
157--172
Opis fizyczny
Bibliogr. 20 poz., il., tab.
Twórcy
autor
  • School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, China
autor
  • School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang, China
autor
  • First Affiliated Hospital, Henan University of Science and Technology, Luoyang, China
Bibliografia
  • [1] X. Li, H. Yao, J. Wang, X. Xu, C. Jiang, and L. Hanzo, “A near-optimal UAV-aided radio coverage strategy for dense urban areas”, IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9098-9109, 2019, doi: 10.1109/TVT.2019.2927425.
  • [2] R. Ding, N. Ujang, H.B. Hamid, M.S.A. Manan, R. Li, S.S.M. Albadareen, and J. Wu, “Application of complex networks theory in urban traffic network researches”, Networks and Spatial Economics, vol. 19, no. 2, pp. 1281–1317, 2019, doi: 10.1007/s11067-019-09466-5.
  • [3] M. Mohammady, H.R. Pourghasemi, and M. Amiri, “Assessment of land subsidence susceptibility in Semnan plain (Iran): A comparison of support vector machine and weights of evidence data mining algorithms”, Natural Hazards, vol. 99, no. 2, pp. 951-971, 2019, doi: 10.1007/s11069-019-03785-z.
  • [4] M. Strauch, A.F. Cord, C. Pätzold, et al., “Constraints in multi-objective optimization of land use allocation-repair or penalize”, Environmental Modelling and Software, vol. 118, pp. 241-251, 2019, doi: 10.1016/j.envsoft.2019.05.003.
  • [5] Z. Masoumi, C.A. Coello, and A. Mansourian, “Dynamic urban land-use change management using multiobjective evolutionary algorithms”, Soft Computing, vol. 24, pp. 4165-4190, 2020, doi: 10.1007/s00500-019-04182-1.
  • [6] Z. Cai, G. Sun, X. Su, T. Li, L. Guo, and Z. Ding, “Visual analysis of land use characteristics around urban rail transit stations”, IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 10, pp. 6221-6231, 2021, doi: 10.1109/TITS.2020.2989811.
  • [7] M. Civera, M.L. Pecorelli, R. Ceravolo, C. Surace, and L.Z. Fragonara, “A multi-objective genetic algorithm strategy for robust optimal sensor placement”, Computer-Aided Civil and Infrastructure Engineering, vol. 36, no. 9, pp. 1185-1202, 2021, doi: 10.1111/mice.12646.
  • [8] X. Deng, M. Li, S. Deng, and L. Wang, “Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification”, Medical and Biological Engineering and Computing, vol. 60, pp. 663-681, 2022, doi: 10.1007/s11517-021-02476-x.
  • [9] R. Priya, D. Ramesh, and V. Udutalapally, “NSGA-2 optimized fuzzy inference system for crop plantation correctness index identification”, IEEE Transactions on Sustainable Computing, vol. 7, no. 1, pp. 172-188, 2022, doi: 10.1109/TSUSC.2021.3064417.
  • [10] Y. Zhang and M. Liu, “Adaptive directed evolved NSGA2 based node placement optimization for wireless sensor networks”, Wireless Networks, vol. 26, no. 5, pp. 3539-3552, 2020, doi: 10.1007/s11276-020-02279-2.
  • [11] T. Chakraborty and X. Lee, “A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability”, International Journal of Applied Earth Observation and Geoinformation, vol. 74, pp. 269-280, 2019, doi: 10.1016/j.jag.2018.09.015.
  • [12] A.M. Hersperger, E. Oliveira, S. Pagliarin, et al., “Urban land-use change: The role of strategic spatial planning”, Global Environmental Change, vol. 51, pp. 32-42, 2018, doi: 10.1016/j.gloenvcha.2018.05.001.
  • [13] M. Barma and U.M. Modibbo, “Multiobjective mathematical optimization model for municipal solid waste management with economic analysis of reuse/recycling recovered waste materials”, Journal of Computational and Cognitive Engineering, vol. 1, no. 3, pp. 122-137, 2022, doi: 10.47852/bonviewJCCE149145.
  • [14] W. He and M. Chen, “Advancing urban life: A systematic review of emerging technologies and artificial intelligence in urban design and planning”, Buildings, vol. 14, no. 3, art. no. 835, 2024, doi: 10.3390/buildings14030835.
  • [15] J. Zan, “Research on robot path perception and optimization technology based on whale optimization algorithm”, Journal of Computational and Cognitive Engineering, vol. 1, no. 4, pp. 201-208, 2022, doi: 10.47852/bonviewJCCE597820205514.
  • [16] U. Choudhury, S.K. Singh, A. Kumar, G. Meraj, P. Kumar, and S. Kanga, “Assessing land use/land cover changes and urban heat island intensification: A case study of Kamrup Metropolitan District, Northeast India (2000-2032)”, Earth, vol. 4, no. 3, pp. 503-521, 2023, doi: 10.3390/earth4030026.
  • [17] L. Albrechts, P. Healey, and K.R.Kunzmann, “Strategic spatial planning and regional governance in Europe”, Journal of the American Planning Association, vol. 69, no. 2, pp. 113-129, 2003, doi: 10.1080/01944360308976301.
  • [18] Q. Pu, X. Zhu, R. Zhang, J. Liu, D. Cai, and G. Fu, “Multiobjective optimization on the operation speed profile design of an urban railway train with a hybrid running strategy”, IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 4, pp. 230-243, 2022, doi: 10.1109/MITS.2021.3066067.
  • [19] L. Yang, J. Li, H.T. Chang, Z. Zhao, H. Ma, and L. Zhou,“ A generative urban space design method based on shape grammar and urban induction patterns”, Land, vol. 12, no. 6, art. no. 1167, 2023, doi: 10.3390/land12061167.
  • [20] R. Bucon and A. Czarnigowska, “Decision support method for optimal modernization of residential buildings”, Archives of Civil Engineering, vol. 70, no. 1, pp. 217-235, 2024, doi: 10.24425/ace.2024.148908.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-30451eb6-4963-4715-bfd2-3676cca1cf4b
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