Volume 13 Issue 2
Jun.  2020
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Article Navigation >  Water Science and Engineering  > 2020  >  13(2): 136-144
Hai-tao Chen, Wen-chuan Wang, Xiao-nan Chen, Lin Qiu. 2020: Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights. Water Science and Engineering, 13(2): 136-144. doi: 10.1016/j.wse.2020.06.005
Citation: Hai-tao Chen, Wen-chuan Wang, Xiao-nan Chen, Lin Qiu. 2020: Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights. Water Science and Engineering, 13(2): 136-144. doi: 10.1016/j.wse.2020.06.005
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Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

doi: 10.1016/j.wse.2020.06.005

  • a School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

  • b Construction and Administration Bureau of South-to-North Water Diversion Middle Route Project, Beijing 100038, China

Funds:  This work was supported by the Foundation of the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province (Grant No. 181RTSTHN009), and the Foundation of the Key Laboratory of Water Environment Simulation and Treatment in Henan Province (Grant No. 2017016).
More Information
  • Corresponding author: Wen-chuan Wang
  • Received Date: 2019-08-25
  • Rev Recd Date: 2020-02-01
    Abstract
  • Based on conventional particle swarm optimization (PSO), this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight (ARIW) strategy, referred to as the ARIW-PSO algorithm, to build a multi-objective optimization model for reservoir operation. Using the triangular probability density function, the inertia weight is randomly generated, and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution, which is suitable for global searches. In the evolution process, the inertia weight gradually decreases, which is beneficial to local searches. The performance of the ARIW-PSO algorithm was investigated with some classical test functions, and the results were compared with those of the genetic algorithm (GA), the conventional PSO, and other improved PSO methods. Then, the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China, including the Panjiakou Reservoir, Daheiting Reservoir, and Taolinkou Reservoir. The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

     

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