Simplex quantum-behaved particle swarm optimization algorithm with application to ecological operation of cascade hydropower reservoirs

Abstract

With growing attentions paid to ecological protection in recent years, the ecological operation of cascade hydropower reservoirs is becoming an increasingly significant problem in water resource system. Mathematically, the ecological operation of cascade hydropower reservoirs is a high-dimensional, nonlinear and strong spatiotemporal coupling constrained optimization problem. To overcome the premature convergence and stagnation search of traditional methods in resolving this problem, this paper develops a novel algorithm known as simplex quantum-behaved particle swarm optimization, where the probabilistic mutation operator is performed on historical best position of some individuals, and then the simplex neighborhood search strategy based on the dynamic probability identification is used to enhance the local exploration ability of the swarm. The numerical experiments of 17 classical test functions indicate that the presented method can achieve satisfactory results in both convergence speed and global search ability. The application results from China’s Wu hydropower system indicate that our method has satisfying performance in reducing the ecological water shortage. Hence, this paper provides a novel effective tool for the complex ecological operation problem of cascade hydropower reservoirs.