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

Lejun Ma, Huan Wang, Baohong Lu and Changjun Qi
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Lejun Ma: Department of Hydrology and Water Resources, Hohai University, 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

Sustainability, 2018, vol. 10, issue 12, 1-15

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.

Keywords: PSO; SCPSO; water balance equation; reservoir optimal operation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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