Comparison of Transboundary Water Resources Allocation Models Based on Game Theory and Multi-Objective Optimization

被引:13
|
作者
Fu, Jisi [1 ]
Zhong, Ping-An [1 ,2 ]
Xu, Bin [1 ]
Zhu, Feilin [1 ]
Chen, Juan [1 ]
Li, Jieyu [1 ]
机构
[1] Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Peoples R China
[2] Hohai Univ, Natl Engn Res Ctr Water Resources Efficient Utili, 1 Xikang Rd, Nanjing 210098, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金; 中国博士后科学基金;
关键词
game theory; multi-objective optimization; water resources allocation; asymmetric Nash-Harsanyi Leader-Follower game model; MANAGEMENT;
D O I
10.3390/w13101421
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Transboundary water resources allocation is an effective measure to resolve water-related conflicts. Aiming at the problem of water conflicts, we constructed water resources allocation models based on game theory and multi-objective optimization, and revealed the differences between the two models. We compare the Pareto front solved by the AR-MOEA method and the NSGA-II method, and analyzed the difference between the Nash-Harsanyi Leader-Follower game model and the multiobjective optimization model. The Huaihe River basin was selected as a case study. The results show that: (1) The AR-MOEA method is better than the NSGA-II method in terms of the diversity metric (Delta); (2) the solution of the asymmetric Nash-Harsanyi Leader-Follower game model is a non-dominated solution, and the asymmetric game model can obtain the same water resources allocation scheme of the multi-objective optimal allocation model under a specific preference structure; (3) after the multi-objective optimization model obtains the Pareto front, it still needs to construct the preference information of the Pareto front for a second time to make the optimal solution of a multi-objective decision, while the game model can directly obtain the water resources allocation scheme at one time by participating in the negotiation. The results expand the solution method of water resources allocation models and provide support for rational water resources allocation.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] Water strategy based on multi-objective planning and Combination incentive game theory
    Liu, Wan-Sheng
    PROCEEDINGS OF THE 2016 2ND WORKSHOP ON ADVANCED RESEARCH AND TECHNOLOGY IN INDUSTRY APPLICATIONS, 2016, 81 : 618 - 621
  • [42] The Multi-objective Water Resources Optimization Scheduling based on Chaos Genetic Algorithm
    Zhao Xiao-qiang
    He Zhi-e
    2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2015, : 4500 - 4505
  • [43] A multi-objective optimization prediction approach for water resources based on swarm intelligence
    Feng Zhang
    Yongheng Zhang
    Earth Science Informatics, 2021, 14 : 457 - 468
  • [44] Multi-objective Optimization of Urban Water Allocation Considering Recycled Water
    Chen, Siwei
    Xu, Yue-Ping
    Guo, Yuxue
    Yu, Xinting
    WATER RESOURCES MANAGEMENT, 2025, : 2615 - 2631
  • [45] A multi-objective optimization prediction approach for water resources based on swarm intelligence
    Zhang, Feng
    Zhang, Yongheng
    EARTH SCIENCE INFORMATICS, 2021, 14 (01) : 457 - 468
  • [46] Automatic multi-objective clustering based on game theory
    Heloulou, Imen
    Radjef, Mohammed Said
    Kechadi, Mohand Tahar
    EXPERT SYSTEMS WITH APPLICATIONS, 2017, 67 : 32 - 48
  • [47] Addressing the contradiction between water supply and demand: a study on multi-objective regional water resources optimization allocation
    Chu, Jingyi
    Wang, Zhaocai
    Bao, Xiaoguang
    Yao, Zhiyuan
    Cui, Xuefei
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2024,
  • [48] Application of Multi-objective Cultural Algorithm in Water Resources Optimization
    Gu, Wei
    Wu, Yonggang
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [49] Multi-objective chaos game optimization
    Khodadadi, Nima
    Abualigah, Laith
    Al-Tashi, Qasem
    Mirjalili, Seyedali
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (20): : 14973 - 15004
  • [50] Multi-objective chaos game optimization
    Nima Khodadadi
    Laith Abualigah
    Qasem Al-Tashi
    Seyedali Mirjalili
    Neural Computing and Applications, 2023, 35 : 14973 - 15004