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Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

Author

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  • Lejun Ma

    (Department of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
    Nanjing Hohai Technology Company, Nanjing 210098, China)

  • Huan Wang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China)

  • Baohong Lu

    (Department of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Changjun Qi

    (Department of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
    Appraisal Center for Environment & Engineering, Ministry of Environmental Protection, Beijing 100012, China)

Abstract

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Suggested Citation

  • Lejun Ma & Huan Wang & Baohong Lu & Changjun Qi, 2018. "Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4445-:d:185887
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    Cited by:

    1. Nazak Rouzegari & Yousef Hassanzadeh & Mohammad Taghi Sattari, 2019. "Using the Hybrid Simulated Annealing-M5 Tree Algorithms to Extract the If-Then Operation Rules in a Single Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3655-3672, August.
    2. Yousif H. Al-Aqeeli & Omar M. A Mahmood Agha, 2020. "Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3099-3112, August.

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