%0 Journal Article
%A WANG Sen
%A MA Zhi-peng
%A LI Shan-zong
%A XIONG Jing
%T Coarse-grained Parallel Adaptive Hybrid Particle Swarm Optimization Algorithm and Its Application to Optimal Operation of Cascaded Reservoirs
%D 2017
%R 10.11988/ckyyb.20151020
%J Journal of Yangtze River Scientific Research Institute
%P 149-154
%V 34
%N 7
%X 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.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20151020