Deriving Water Allocation Schemes for Interbasin Water Transfer Projects Using a Novel Multiobjective Cuckoo Search Algorithm
Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 6
Abstract
Interbasin water transfer projects (IBWTPs) have become an important measure to alleviate regional water stresses by artificially regulating water resources between water-rich and water-scarce areas. However, there are many conflicting objectives and complex practical operational constraints that complicate the derivation of water allocation schemes for IBWTPs. Here, we propose a multiobjective optimization methodology for deriving water allocation schemes for IBWTPs, which includes three modules: (1) formulating a multiobjective optimal water allocation problem for IBWTPs based on practical operational constraints; (2) proposing a novel multiobjective cuckoo search (NMOCS) algorithm and combining it with a simulation-optimization approach to solve the water allocation problem; and (3) filtering the Pareto solutions using the analytic hierarchy process (AHP)-entropy method. Based on the multiobjective cuckoo search (MOCS) algorithm, the NMOCS employs four improvement strategies, including a population initialization strategy, flock search strategy, multistrategy external archive maintenance strategy, and adaptive parameters, to improve the convergence property and diversity of solutions. To test the performance of the NMOCS, we considered the MOCS and four widely used multiobjective evolutionary algorithms (MOEAs) as a comparison and employed these six MOEAs to solve 11 multiobjective mathematical benchmark problems as well as the water allocation problems of the Jiangsu Province, China, section of the South-to-North Water Diversion Project under normal, dry, and extremely dry hydrological conditions. The results show that the NMOCS outperformed other MOEAs in handling multiobjective mathematical benchmark problems, especially in ZDT1, ZDT2, ZDT6, DTLZ2, DTLZ4, and DTLZ7. Compared with other MOEAs, the NMOCS did not always capture the highest percentage of the reference water allocation schemes, but it provided more than 18% (greater than one-sixth) of effective water allocation schemes for all hydrologic conditions. Meanwhile, compared with optimal water allocation schemes derived from other MOEAs, the NMOCS effectively improved the operational performance under normal and drought hydrological conditions, especially in the total water pumping and the water withdrawn from the Yangtze River. This research can help to update our understanding of MOEAs, particularly the MOCS, and serve as a reference for better-allocating water resources in IBWTPs.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was supported by the Jiangsu Province Water Science and Technology Key Projects (2020005). We are also grateful to the reviewers for their useful comments that significantly improved the current version of this paper.
References
Alkebsi, K., and W. Du. 2020. “A fast multi-objective particle swarm optimization algorithm based on a new archive updating mechanism.” IEEE Access 8 (Jul): 124734–124754. https://doi.org/10.1109/ACCESS.2020.3007846.
Cheng, J., L. Wang, and Y. Xiong. 2018. “Modified cuckoo search algorithm and the prediction of flashover voltage of insulators.” Neural Comput. Appl. 30 (2): 355–370. https://doi.org/10.1007/s00521-017-3179-1.
Das, I., and J. E. Dennis. 1998. “Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems.” SIAM J. Optim. 8 (3): 631–657. https://doi.org/10.1137/S1052623496307510.
Deb, K., and H. Jain. 2014. “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints.” IEEE Trans. Evol. Comput. 18 (4): 577–601. https://doi.org/10.1109/TEVC.2013.2281535.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
Doppler, W., A. Z. Salman, E. K. Al-Karablieh, and H.-P. Wolff. 2002. “The impact of water price strategies on the allocation of irrigation water: The case of the Jordan Valley.” Agric. Water Manage. 55 (3): 171–182. https://doi.org/10.1016/S0378-3774(01)00193-7.
Duan, K., S. Qu, N. Liu, G. R. Dobbs, P. V. Caldwell, and G. Sun. 2023. “Evolving efficiency of inter-basin water transfers in regional water stress alleviation.” Resour. Conserv. Recycl. 191 (Apr): 106878. https://doi.org/10.1016/j.resconrec.2023.106878.
Guo, X., T. Hu, T. Zhang, and Y. Lv. 2012. “Bilevel model for multi-reservoir operating policy in inter-basin water transfer-supply project.” J. Hydrol. 424 (Mar): 252–263. https://doi.org/10.1016/j.jhydrol.2012.01.006.
Guo, Y., X. Tian, G. Fang, and Y.-P. Xu. 2020. “Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers.” Adv. Water Resour. 138 (Apr): 103531. https://doi.org/10.1016/j.advwatres.2020.103531.
He, C., Z. Liu, J. Wu, X. Pan, Z. Fang, J. Li, and B. A. Bryan. 2021. “Future global urban water scarcity and potential solutions.” Nat. Commun. 12 (1): 4667. https://doi.org/10.1038/s41467-021-25026-3.
Hua, Y., Q. Liu, K. Hao, and Y. Jin. 2021. “A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts.” IEEE/CAA J. Autom. Sin. 8 (2): 303–318. https://doi.org/10.1109/JAS.2021.1003817.
Jafarzadegan, K., A. Abed-Elmdoust, and R. Kerachian. 2013. “A fuzzy variable least core game for inter-basin water resources allocation under uncertainty.” Water Resour. Manage. 27 (9): 3247–3260. https://doi.org/10.1007/s11269-013-0344-x.
Li, M., X. Yang, F. Wu, and P. Babuna. 2022. “Spatial equilibrium-based multi-objective optimal allocation of regional water resources.” J. Hydrol.: Reg. Stud. 44 (Dec): 101219. https://doi.org/10.1016/j.ejrh.2022.101219.
Li, W., A. Sankarasubramanian, R. S. Ranjithan, and E. D. Brill. 2014. “Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework.” Water Resour. Res. 50 (8): 6810–6827. https://doi.org/10.1002/2013WR015248.
Li, Y., Q. Cui, C. Li, X. Wang, Y. Cai, G. Cui, and Z. Yang. 2017. “An improved multi-objective optimization model for supporting reservoir operation of China’s South-to-North Water Diversion Project.” Sci. Total Environ. 575 (Jan): 970–981. https://doi.org/10.1016/j.scitotenv.2016.09.165.
Liu, D., T. Bai, M. Deng, Q. Huang, X. Wei, and J. Liu. 2023. “A parallel approximate evaluation-based model for multi-objective operation optimization of reservoir group.” Swarm Evol. Comput. 78 (Apr): 101288. https://doi.org/10.1016/j.swevo.2023.101288.
Luo, J., Y. Yang, X. Li, Q. Liu, M. Chen, and K. Gao. 2018. “A decomposition-based multi-objective evolutionary algorithm with quality indicator.” Swarm Evol. Comput. 39 (Apr): 339–355. https://doi.org/10.1016/j.swevo.2017.11.004.
Ma, T., et al. 2020a. “Pollution exacerbates China’s water scarcity and its regional inequality.” Nat. Commun. 11 (1): 650. https://doi.org/10.1038/s41467-020-14532-5.
Ma, Y., J. Chang, A. Guo, L. Wu, J. Yang, and L. Chen. 2020b. “Optimizing inter-basin water transfers from multiple sources among interconnected river basins.” J. Hydrol. 590 (Nov): 125461. https://doi.org/10.1016/j.jhydrol.2020.125461.
Makhloufi, S., S. Khennas, S. Bouchaib, and A. H. Arab. 2022. “Multi-objective cuckoo search algorithm for optimized pathways for 75 % renewable electricity mix by 2050 in Algeria.” Renewable Energy 185 (Feb): 1410–1424. https://doi.org/10.1016/j.renene.2021.10.088.
Mendoza Ramírez, R., M. L. Arganis Juárez, R. Domínguez Mora, L. D. Padilla Morales, Ó. A. Fuentes Mariles, A. Mendoza Reséndiz, E. Carrizosa Elizondo, and R. B. Carmona Paredes. 2021. “Operation policies through dynamic programming and genetic algorithms, for a reservoir with irrigation and water supply uses.” Water Resour. Manage. 35 (5): 1573–1586. https://doi.org/10.1007/s11269-021-02802-w.
Meng, X., J. Chang, X. Wang, and Y. Wang. 2019. “Multi-objective hydropower station operation using an improved cuckoo search algorithm.” Energy 168 (Feb): 425–439. https://doi.org/10.1016/j.energy.2018.11.096.
Ming, B., J. Chen, W. Fang, P. Liu, W. Zhang, and J. Jiang. 2023. “Evaluation of stochastic optimal operation models for hydro–photovoltaic hybrid generation systems.” Energy 267 (Mar): 126500. https://doi.org/10.1016/j.energy.2022.126500.
Ming, B., P. Liu, J. Chang, Y. Wang, and Q. Huang. 2017. “Deriving operating rules of pumped water storage using multiobjective optimization: Case study of the Han to Wei Interbasin Water Transfer Project, China.” J. Water Resour. Plann. Manage. 143 (10): 05017012. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000828.
Nagra, A. A., F. Han, and Q. H. Ling. 2019. “An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search.” Eng. Optim. 51 (7): 1115–1132. https://doi.org/10.1080/0305215X.2018.1525709.
Nikoo, M. R., A. Karimi, and R. Kerachian. 2013. “Optimal long-term operation of reservoir-river systems under hydrologic uncertainties: Application of interval programming.” Water Resour. Manage. 27 (11): 3865–3883. https://doi.org/10.1007/s11269-013-0384-2.
Niu, X. 2022. “The first stage of the middle-line South-To-North Water-Transfer Project.” Engineering 16 (Sep): 21–28. https://doi.org/10.1016/j.eng.2022.07.001.
Pankaj, B. S., M. N. Naidu, A. Vasan, and M. R. Varma. 2020. “Self-adaptive cuckoo search algorithm for optimal design of water distribution systems.” Water Resour. Manage. 34 (10): 3129–3146. https://doi.org/10.1007/s11269-020-02597-2.
Pigram, J. J. 2000. “Options for rehabilitation of Australia’s Snowy River: An economic perspective.” Regul. Rivers: Res. Manage. 16 (4): 363–373. https://doi.org/10.1002/1099-1646(200007/08)16:4%3C363::AID-RRR610%3E3.0.CO;2-I.
Randall, D., L. Cleland, C. S. Kuehne, G. W. Link, and D. P. Sheer. 1997. “Water supply planning simulation model using mixed-integer linear programming ‘engine’.” J. Water Resour. Plann. Manage. 123 (2): 116–124. https://doi.org/10.1061/(ASCE)0733-9496(1997)123:2(116).
Rani, D., S. Mourato, and M. Moreira. 2020. “A generalized dynamic programming modelling approach for integrated reservoir operation.” Water Resour. Manage. 34 (4): 1335–1351. https://doi.org/10.1007/s11269-020-02505-8.
Reda, M., A. Onsy, M. A. Elhosseini, A. Y. Haikal, and M. Badawy. 2022. “A discrete variant of cuckoo search algorithm to solve the travelling salesman problem and path planning for autonomous trolley inside warehouse.” Knowledge-Based Syst. 252 (Sep): 109290. https://doi.org/10.1016/j.knosys.2022.109290.
Ren, K., T. Bai, and Q. Huang. 2023. “Scale-invariant sensitivity for multi-purpose water reservoirs management with temporal scale-dependent modeling.” J. Environ. Manage. 339 (Aug): 117862. https://doi.org/10.1016/j.jenvman.2023.117862.
Ren, K., S. Huang, Q. Huang, H. Wang, G. Leng, and Y. Wu. 2019. “Defining the robust operating rule for multi-purpose water reservoirs under deep uncertainties.” J. Hydrol. 578 (Nov): 124134. https://doi.org/10.1016/j.jhydrol.2019.124134.
Richards, C. E., A. Tzachor, S. Avin, and R. Fenner. 2023. “Rewards, risks and responsible deployment of artificial intelligence in water systems.” Nature Water 1 (5): 422–432. https://doi.org/10.1038/s44221-023-00069-6.
Sinha, P., E. Rollason, L. J. Bracken, J. Wainwright, and S. M. Reaney. 2020. “A new framework for integrated, holistic, and transparent evaluation of inter-basin water transfer schemes.” Sci. Total Environ. 721 (Jun): 137646. https://doi.org/10.1016/j.scitotenv.2020.137646.
Sunkara, S. V., and R. Singh. 2022. “Assessing the impact of the temporal resolution of performance indicators on optimal decisions of a water resources system.” J. Hydrol. 612 (Sep): 128185. https://doi.org/10.1016/j.jhydrol.2022.128185.
Valerio, C., M. Giuliani, A. Castelletti, A. Garrido, and L. De Stefano. 2023. “Multi-objective optimal design of interbasin water transfers: The Tagus-Segura aqueduct (Spain).” J. Hydrol.: Reg. Stud. 46 (Apr): 101339. https://doi.org/10.1016/j.ejrh.2023.101339.
Valian, E., S. Tavakoli, S. Mohanna, and A. Haghi. 2013. “Improved cuckoo search for reliability optimization problems.” Comput. Ind. Eng. 64 (1): 459–468. https://doi.org/10.1016/j.cie.2012.07.011.
Wan, W., X. Guo, X. Lei, Y. Jiang, and H. Wang. 2018. “A novel optimization method for multi-reservoir operation policy derivation in complex inter-basin water transfer system.” Water Resour. Manage. 32 (1): 31–51. https://doi.org/10.1007/s11269-017-1735-1.
Wang, H., B. Sheng, Q. Lu, R. Luo, and G. Fu. 2020. “A multi-objective cuckoo search algorithm based on the record matrix for a mixed-model assembly line car-sequencing problem.” IEEE Access 8 (Apr): 76453–76470. https://doi.org/10.1109/ACCESS.2020.2989627.
Wang, J. 2020. “Pareto fronts-driven multi-objective cuckoo search for 5G network optimization.” KSII Trans. Internet Inf. Syst. 14 (7): 2800–2814. https://doi.org/10.3837/tiis.2020.07.004.
Wang, Z., and Y. Li. 2015. “Irreversibility analysis for optimization design of plate fin heat exchangers using a multi-objective cuckoo search algorithm.” Energy Convers. Manage. 101 (Sep): 126–135. https://doi.org/10.1016/j.enconman.2015.05.009.
Wang, Z., Y. Pei, and J. Li. 2023. “A survey on search strategy of evolutionary multi-objective optimization algorithms.” Appl. Sci. 13 (7): 4643. https://doi.org/10.3390/app13074643.
Wu, Z., X. Zhao, Y. Ma, and X. Zhao. 2019. “A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting.” Appl. Energy 237 (Mar): 896–909. https://doi.org/10.1016/j.apenergy.2019.01.046.
Yang, Q., P. Liu, J. Zhang, and N. Dong. 2022. “Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation.” Appl. Energy 307 (Feb): 118057. https://doi.org/10.1016/j.apenergy.2021.118057.
Yang, X.-S., and S. Deb. 2009. “Cuckoo search via Lévy flights.” In Proc., 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210–214. New York: IEEE.
Yang, X.-S., and S. Deb. 2013. “Multiobjective cuckoo search for design optimization.” Comput. Oper. Res. 40 (6): 1616–1624. https://doi.org/10.1016/j.cor.2011.09.026.
Yang, Z., X. Huang, G. Fang, J. Ye, and C. Lu. 2021. “Benefit evaluation of east route project of south to north water transfer based on trapezoid cloud model.” Agric. Water Manage. 254 (Aug): 106960. https://doi.org/10.1016/j.agwat.2021.106960.
Yao, H., Z. Dong, D. Li, X. Ni, T. Chen, M. Chen, W. Jia, and X. Huang. 2022. “Long-term optimal reservoir operation with tuning on large-scale multi-objective optimization: Case study of cascade reservoirs in the Upper Yellow River Basin.” J. Hydrol.: Reg. Stud. 40 (Apr): 101000. https://doi.org/10.1016/j.ejrh.2022.101000.
Ye, X., Y. Wang, A. Guo, X. Wang, M. Zhao, B. He, Z. Li, C. Niu, and Q. Wang. 2023. “Optimal operation of inter basin water transfer under the form of water sources interconnection with connected tunnel.” J. Hydrol.: Reg. Stud. 45 (Feb): 101320. https://doi.org/10.1016/j.ejrh.2023.101320.
Yin, D., X. Li, F. Wang, Y. Liu, B. F. W. Croke, and A. J. Jakeman. 2022. “Water-energy-ecosystem nexus modeling using multi-objective, non-linear programming in a regulated river: Exploring tradeoffs among environmental flows, cascaded small hydropower, and inter-basin water diversion projects.” J. Environ. Manage. 308 (Apr): 114582. https://doi.org/10.1016/j.jenvman.2022.114582.
Zeff, H. B., A. L. Hamilton, K. Malek, J. D. Herman, J. S. Cohen, J. Medellin-Azuara, P. M. Reed, and G. W. Characklis. 2021. “California’s food-energy-water system: An open source simulation model of adaptive surface and groundwater management in the Central Valley.” Environ. Modell. Software 141 (Jul): 105052. https://doi.org/10.1016/j.envsoft.2021.105052.
Zhang, M., H. Wang, Z. Cui, and J. Chen. 2018. “Hybrid multi-objective cuckoo search with dynamical local search.” Memet. Comput. 10 (2): 199–208. https://doi.org/10.1007/s12293-017-0237-2.
Zhang, Q., and H. Li. 2007. “MOEA/D: A multiobjective evolutionary algorithm based on decomposition.” IEEE Trans. Evol. Comput. 11 (6): 712–731. https://doi.org/10.1109/TEVC.2007.892759.
Zhou, K. 2021. “Multi-objective water resources optimum allocation scheme based on improved standard cuckoo searching algorithm (ISCSA).” Water Supply 22 (10): 7893–7903. https://doi.org/10.2166/ws.2022.310.
Zhou, X., Y. Liu, and B. Li. 2016. “A multi-objective discrete cuckoo search algorithm with local search for community detection in complex networks.” Mod. Phys. Lett. B 30 (07): 1650080. https://doi.org/10.1142/S0217984916500809.
Zitzler, E., and S. Künzli. 2004. “Indicator-based selection in multiobjective search.” In Vol. 3242 of Parallel problem solving from nature - PPSN VIII. PPSN 2004. Lecture notes in computer science, 832–842. Berlin: Springer.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 150 • Issue 6 • June 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 4, 2023
Accepted: Jan 15, 2024
Published online: Mar 29, 2024
Published in print: Jun 1, 2024
Discussion open until: Aug 29, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.