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A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching

Author

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  • Xu Wu

    (North Minzu University)

  • Xiaojing Shen

    (Ningxia University
    Ningxia University
    Ningxia University)

  • Chuanjiang Wei

    (China Institute of Water Resources and Hydropower Research (IWHR))

  • Xinmin Xie

    (China Institute of Water Resources and Hydropower Research (IWHR))

  • Jianshe Li

    (Ningxia University)

Abstract

Reservoir ecological dispatching is a complex system problem involving multiple objectives, multiple criteria and multiple phases. This study established a multiobjective ecological dispatching model of the Yinma River Basin in Changchun city based on the water demand, socioeconomic development, river ecology, and constraints on reservoir characteristic parameters. Taking advantage of particle swarm optimization (PSO) and genetic algorithm (GA), a PSO-GA hybrid algorithm is proposed and applied to solve the schemes of ecological dispatching models considering different ecological flow requirements. The annual mean scheduling results show that the three scheduling schemes basically achieve the objectives of river ecological base flow scheduling. For ecologically suitable flows, the guaranteed rates for the RGOS1, RGOS2, and RGOS3 schedules at the Dehui section were 78.35%, 86.36%, and 95.98%, respectively, whereas the rates were 81.77%, 90.13%, and 96.57%, respectively, at the Nong’an section. The scheduling results of typical years show that the water security situation in the study area is not optimal, but the river ecological environment can be considerably improved by reservoir ecological dispatching. Finally, the excellent performance of the hybrid PSO-GA proposed in this study is verified via comparison with other algorithms. The Pareto front optimized by the PSO-GA can dominate the Pareto front solutions of the other algorithms. The IGD (0.19) of the Pareto front optimized by the PSO-GA is the smallest, and the SP (0.83) and HV (0.93) are the largest, indicating better convergence and comprehensive performance.

Suggested Citation

  • Xu Wu & Xiaojing Shen & Chuanjiang Wei & Xinmin Xie & Jianshe Li, 2024. "A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 2229-2249, April.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:6:d:10.1007_s11269-024-03755-6
    DOI: 10.1007/s11269-024-03755-6
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    References listed on IDEAS

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    1. Mirzaie, Nargis & Banihabib, Mohammad Ebrahim & shahdany, S. Mehdy hashemy & Randhir, Timothy O., 2021. "Fuzzy particle swarm optimization for conjunctive use of groundwater and reclaimed wastewater under uncertainty," Agricultural Water Management, Elsevier, vol. 256(C).
    2. Hu Hu & Kan Yang & Lang Liu & Lyuwen Su & Zhe Yang, 2019. "Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2357-2379, May.
    3. Zhe Yang & Kan Yang & Hu Hu & Lyuwen Su, 2019. "The Cascade Reservoirs Multi-Objective Ecological Operation Optimization Considering Different Ecological Flow Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 207-228, January.
    4. Reza Sepahvand & Hamid R. Safavi & Farshad Rezaei, 2019. "Multi-Objective Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2123-2137, April.
    5. Kalyanmoy Deb & Nikhil Padhye, 2014. "Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms," Computational Optimization and Applications, Springer, vol. 57(3), pages 761-794, April.
    6. Bungon Kumphon, 2013. "Genetic Algorithms for Multi-objective Optimization: Application to a Multi-reservoir System in the Chi River Basin, Thailand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4369-4378, September.
    7. Wenhua Wan & Xuning Guo & Xiaohui Lei & Yunzhong Jiang & Hao Wang, 2018. "A Novel Optimization Method for Multi-Reservoir Operation Policy Derivation in Complex Inter-Basin Water Transfer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 31-51, January.
    8. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.
    9. I. Tsoukalas & C. Makropoulos, 2015. "A Surrogate Based Optimization Approach for the Development of Uncertainty-Aware Reservoir Operational Rules: the Case of Nestos Hydrosystem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4719-4734, October.
    10. Arya Yaghoubzadeh-Bavandpour & Omid Bozorg-Haddad & Mohammadreza Rajabi & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Application of Swarm Intelligence and Evolutionary Computation Algorithms for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2275-2292, May.
    11. Xiaojing Shen & Xu Wu & Xinmin Xie & Chuanjiang Wei & Liqin Li & Jingjing Zhang, 2021. "Synergetic Theory-Based Water Resource Allocation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2053-2078, May.
    12. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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