Abstract: To improve the computing efficiency of optimal operation of large-scale cascaded reservoirs, a coarse-grained parallel adaptive hybrid particle swarm optimization (PAHPSO) algorithm is proposed in full use of the popular multi-core computers. The method is based on adaptive hybrid particle swarm optimization (AHPSO) algorithm, and adopts the coarse-grain model and divide-and-conquer strategy of Fork/Join multi-core parallel framework to divide the initial population into multiple small-scale subpopulations, which are assigned to different logical threads averagely for parallel computing. After the optimization computation for all subpopulations, the optimization result sets are merged to obtain the globally optimal solution. The proposed algorithm is applied to the generation and operation of cascaded reservoirs located on the lower stream of Lancang River. Results show that the method gives full play to multi-core computer performance, and the maximum speedup in 4-core parallel environment reaches 3.97 with the time-consuming cutting down by 1 787.2 s. The computing efficiency has improved significantly and it provides a feasible and efficient solution for the optimal operation of increasingly expanding large-scale cascaded reservoirs in China.

Key words: cascaded reservoirs, optimal operation, coarse-grain, multi-core parallel, Fork/Join, particle swarm optimization algorithm