Improved quantum-behaved particle swarm optimization and its application in optimal operation of hydropower stations

This paper develops an improved quantum-behaved particle swarm optimization (IQPSO) to alleviate the existing defects of the quantum-behaved particle swarm optimization, such as premature convergence and poor search ability. Firstly, the chaotic search is employed to promote the quality of initial population. Secondly, the evolution pattern of the population is improved by using the weighted updating the population mean best position. Thirdly, the mutation operator is introduced to ensure the global searching ability. Moreover, according to the peculiarities of the optimization problem, the matrix real-coded particle and the complex constraints handling method are designed to further enhance the search efficiency. The proposed method is applied to the optimal operation in Wujiang River cascade hydropower stations. The results demonstrate the effectiveness and practicality of the proposed method in engineering applications. Compared to the progressive optimality algorithm, the spilling discharge and computation time of IQPSO in long-term simulation are reduced by 8.9％ and 72.3％, respectively. IQPSO is an efficient and practical approach for the optimal operation of large-scale hydropower system.