【摘要】：流域最佳管理措施(beneficial management practices,BMPs)情景優(yōu)化問題是一個典型的復(fù)雜地理計算問題,目前所常用的BMPs情景優(yōu)化算法需要結(jié)合流域模型進行大量的迭代運算,因而花費大量計算時間,難以滿足實際應(yīng)用的要求。本文針對目前代表性的BMPs情景優(yōu)化算法——ε支配多目標遺傳算法(ε-NSGA-II),采用主從式并行策略,利用MPI并行編程庫實現(xiàn)了該優(yōu)化算法的并行化。在江西省贛江上游的梅川江流域(面積為6 366km2)進行BMPs情景優(yōu)化的應(yīng)用案例表明,并行化的優(yōu)化算法當運行于集群機時,加速比隨著核數(shù)(8~512核)的增加而遞增,當核數(shù)為512時,加速比達到最大值(310);并行效率隨著核數(shù)的增加逐漸下降,最高值0.91,最低值0.61,取得了明顯的加速效果。
[Abstract]:Optimal watershed management (beneficial management practices,BMPs) scenario optimization problem is a typical complex geographical calculation problem. The commonly used BMPs scenario optimization algorithm needs a large number of iterations combined with watershed model. Therefore, it takes a lot of computing time and is difficult to meet the requirements of practical application. In this paper, the BMPs scenario optimization algorithm 蔚 -dominated multi-objective genetic algorithm (蔚 -NSGA-II) is used to realize the parallelization of the optimization algorithm by using the MPI parallel programming library and the master-slave parallel strategy. The application of BMPs scenario optimization in the Meichuan River basin (area 6 366km2) in the upper reaches of Ganjiang River in Jiangxi Province shows that the speedup ratio of the parallel optimization algorithm increases with the increase of the number of kernels (8512cores) when it runs on the cluster computer. When the kernel number is 512, the speedup ratio reaches the maximum (310). The parallel efficiency decreases gradually with the increase of the number of kernels, the highest value is 0.91and the lowest value is 0.61.The acceleration effect is obvious.
【作者單位】： 中國科學(xué)院地理科學(xué)與資源研究所;中國科學(xué)院大學(xué);加拿大圭爾夫大學(xué)地理系;南京師范大學(xué)地理科學(xué)學(xué)院;美國威斯康星大學(xué)麥迪遜分校地理系;
【基金】：國家863計劃(2011AA120305) 國家科技支撐計劃(2013BAC08B03-4) 國家水專項計劃(2013ZX07103006-005)~~
【分類號】：TV213.4
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本文編號：2325555