长江科学院院报2017，Vol.34Issue(7)：149-154,6.DOI:10.11988/ckyyb.20151020
粗粒度并行自适应混合粒子群算法及其在梯级水库群优化调度中的应用
Coarse-grained Parallel Adaptive Hybrid Particle Swarm OptimizationAlgorithm and Its Application to Optimal Operation ofCascaded Reservoirs
摘要
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.关键词
梯级水库群/优化调度/粗粒度/多核并行/Fork/Join/粒子群算法Key words
cascaded reservoirs/optimal operation/coarse-grain/multi-core parallel/Fork/Join/particle swarm optimization algorithm分类
建筑与水利引用本文复制引用
王森,马志鹏,李善综,熊静..粗粒度并行自适应混合粒子群算法及其在梯级水库群优化调度中的应用[J].长江科学院院报,2017,34(7):149-154,6.基金项目
国家重点研发计划资助项目(2017YFC0405905) （2017YFC0405905）
水利部公益性行业科研专项(201401013,201501010) （201401013,201501010）
