Skip to main content
SpringerLink
Log in

Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

The operation of a reservoir system for flood resources utilization is a complex problem as it involves many variables, a large number of constraints and multiple objectives. In this paper, a new algorithm named multi-objective cultured evolutionary algorithm based on decomposition (MOCEA/D) is proposed for optimizing the hierarchical flood operation rules (HFORs) with four objectives: upstream flood control, downstream flood control, power generation and navigation. The performance of MOCEA/D is validated through some well-known benchmark problems. On achieving satisfactory performance, MOCEA/D is applied to a case study of HFORs optimization for Three Gorges Project (TGP). The experimental results show that MOCEA/D obtains a uniform non-dominated schemes set. The optimized HFORs can improve the power generation and navigation rate as much as possible under the premise of ensuring flood control safety for small and medium floods (smaller than 1% frequency flood). The obtained results show that MOCEA/D can be a viable alternative for generating multi-objective HFORs for water resources planning and management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Baltar AM, Fontane DG (2008) Use of Multiobjective Particle Swarm Optimization in Water Resources Management. J Water Resour Plan Manag 134(3):257–265

    Article  Google Scholar 

  • Chen L, McPhee J, Yeh WWG (2007) A diversified multiobjective GA for optimizing reservoir rule curves. Adv Water Resour 30(5):1082–1093

    Article  Google Scholar 

  • Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36(2):219–236

    Article  Google Scholar 

  • Cutter SL, Ismail-Zadeh A, Alcántara-Ayala I, Altan O, Baker DN, Briceño S, Gupta H, Holloway A, Johnston D, McBean GA, Ogawa Y, Paton D, Porio E, Silbereisen RK, Takeuchi K, Valsecchi GB, Vogel C, Wu G (2015) Global risks: Pool knowledge to stem losses from disasters. Nature 522(7556):277–279

    Article  Google Scholar 

  • Das I, Dennis JE (1998) Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J Optim 8:631–657

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable Test Problems for Evolutionary Multi-Objective Optimization. In: Abraham A, Jain L, Goldberg R (eds) Evolutionary Multiobjective Optimization. Springer-Verlag, London, pp 105–145

    Chapter  Google Scholar 

  • Deb K, Jain H (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 18:577–601

    Article  Google Scholar 

  • Ding W, Zhang C, Peng Y, Zeng R, Zhou H, Cai X (2015) An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk. Water Resour Res 51(6):4702–4726

    Article  Google Scholar 

  • Fonseca CM and Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation discussion and generalization. In: Forrest S (ed) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, pp 416–423. Morgan Kaufmann

  • Jia B, Simonovic SP, Zhong P, Yu Z (2016) A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System. Water Resour Manag 30(10):3363–3387

    Article  Google Scholar 

  • Karamouz M, Houck MH (1982) Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resour Res 18(5):1337–1344

    Article  Google Scholar 

  • Landa Becerra R, Coello CAC (2006) Cultured differential evolution for constrained optimization. Comput Method Appl M 195(33–36):4303–4322

    Article  Google Scholar 

  • Liu Y, Ye L, Qin H, Hong X, Ye J, Yin X (2018) Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression. J Hydrol 561:146–159

    Article  Google Scholar 

  • Marien JL, Damázio JM, Costa FS (1994) Building flood control rule curves for multipurpose multireservoir systems using controllability conditions. Water Resour Res 30(4):1135–1144

    Article  Google Scholar 

  • Needham JT, Watkins DW, Lund JR, Nanda SK (2000) Linear Programming for Flood Control in the Iowa and Des Moines Rivers. J Water Resour Plan Manag 126(3):118–127

    Article  Google Scholar 

  • Qin H, Zhou J, Lu Y, Li Y, Zhang Y (2010) Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in Reservoir Flood Control Operation. Water Resour Manag 24(11):2611–2632

    Article  Google Scholar 

  • Reddy MJ, Kumar DN (2006) Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm. Water Resour Manag 20(6):861–878

    Article  Google Scholar 

  • Renfrew A. C (1994) “Dynamic Modeling in Archaeology: What, When, and Where?” Dynamical Modeling and the Study of Change in Archaeology, S. E. van der Leeuw, ed., Edinburgh University Press

  • Reynolds RG (1994) An Introduction to Cultural Algorithms. In: Sebalk AVF (ed) Proceedings of the 3th annual conference on evolution programming. World Scientific, River Edge, pp 131–136

    Google Scholar 

  • Storn R, Price K (1997) Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  Google Scholar 

  • Wei C, Hsu N (2009) Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. J Hydrol 365(3–4):213–224

    Article  Google Scholar 

  • Yeh WWG (1985) Reservoir Management and Operations Models: A State-of-the-Art Review. Water Resour Res 21(12):1797–1818

    Article  Google Scholar 

  • Zhang J, Liu P, Wang H, Lei X, Zhou Y (2015) A Bayesian model averaging method for the derivation of reservoir operating rules. J Hydrol 528:276–285

    Article  Google Scholar 

  • Zhang Q, Li H (2007) MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans Evol Comput 11(6):721–731

    Google Scholar 

  • Zhou Y, Guo S, Xu C, Liu P, Qin H (2015) Deriving joint optimal refill rules for cascade reservoirs with multi-objective evaluation. J Hydrol 524:166–181

    Article  Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm. Paper presented at the Computer Engineering and Networks Laboratory (TIK), Gloriastrasse 35, CH-8092 Zurich, Switzerland

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 91647114, No. 51779013, 51479075), the Natural Science Foundation of Hubei Province (2017CFB613), the Fundamental Research Funds for the Central Universities (HUST: 2016YXZD047), and special thanks are given to the anonymous reviewers and editors for their constructive comments.

Author information

Authors and Affiliations

  1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Yongqi Liu, Hui Qin, Li Mo & Xingli Yin

  2. Changjiang River Scientific Research Institute, Wuhan, Hubei, China

    Yongqiang Wang & Duan Chen

  3. Three Gorges Cascade Dispatch and Communication Center, Yichang, Hubei, China

    Shusen Pang

Authors
  1. Yongqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongqiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Duan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shusen Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xingli Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Qin.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Qin, H., Mo, L. et al. Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition. Water Resour Manage 33, 337–354 (2019). https://doi.org/10.1007/s11269-018-2105-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-018-2105-3

Keywords

Search

Navigation