Multiple groups of gradient particle swarm optimization and its application in optimal operation of reservoir

In this paper, the particle swarm optimization algorithm (PSO) for reservoir optimal operation is studied. A new algorithm which is suitable for reservoir optimal operation called multiple groups of gradient particle swarm optimization algorithm (MGPSO) is proposed to avoid the shortcomings of PSO including premature convergence, poor search accuracy and easily falling into local optimal solution. The gradient searching strategy is introduced to improve the search accuracy of local optima. Grouping and randomly updating strategy are used to improve the searching ability of global optima. Simulation experiments and the example of reservoir optimal operation show that the new algorithm MGPSO obviously outperforms the standard PSO and shuffled frog leaping particle swarm optimization (SFLPSO), and is effective in solving the optimal operation of hydropower station reservoir.