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.
Highlights
Water is a limited non-renewable energy source and a major constraint on social development
Our results presented here demonstrate that the strongly constrained particle swarm optimization algorithm (SCPSO) algorithm has performed well in results, convergence, and convergence speed of the computational results compared with the particle swarm optimization (PSO) algorithm
We constructed a non-inferior solution space to improve the efficiency of particle optimization based on the water balance equation, and the results showed that SCPSO has more than 50% of effective particles at the beginning of iterations, and the number of final effective particles is more than twice that of PSO, which greatly improves the efficiency of optimization
Summary
Water is a limited non-renewable energy source and a major constraint on social development. The main reason is that the evolutionary algorithm adopts random search mode and the reservoir multi-objective optimization decision has less feasible space, which makes the search ability of the evolutionary algorithm in dealing with the optimal operation of reservoirs is limited, and it is difficult to achieve satisfactory results [20,21,22,23]. It is a common method to solve reservoir optimization problems by using various algorithms, such as GA-PSO, PSO-EDA (estimation of distribution algorithm), and PSO-BP (back-propagation) [31,32,33,34] These studies play an important role in perfecting particle swarm optimization. Others try to improve the feasible domain of the genetic algorithm to optimize the water and sand transfer problem [40,41] These studies improve the efficiency of the algorithm by reducing the search space to a certain extent. Compared with DP and traditional PSO, the results showed that SCPSO is more efficient and satisfactory in seeking an optimal strategy
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