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

Xu Wu, Xiaojing Shen (), Chuanjiang Wei, Xinmin Xie and Jianshe Li
Additional contact information
Xu Wu: North Minzu University
Xiaojing Shen: 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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 6, No 20, 2229-2249

Abstract: 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.

Keywords: Reservoir Ecological Dispatching; Multiobjective; Particle Swarm Optimization; Genetic Algorithm (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-03755-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:6:d:10.1007_s11269-024-03755-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-03755-6

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().