流域最佳管理措施情景优化算法的并行化

流域最佳管理措施(beneficial management practices, BMPs)情景优化问题是一个典型的复杂地理计算问题,目前所常用的BMPs情景优化算法需要结合流域模型进行大量的迭代运算,因而花费大量计算时间,难以满足实际应用的要求。本文针对目前代表性的BMPs情景优化算法——ε支配多目标遗传算法(ε-NSGA-Ⅱ),采用主从式并行策略,利用MPI并行编程库实现了该优化算法的并行化。在江西省赣江上游的梅川江流域(面积为6366 km²)进行BMPs情景优化的应用案例表明,并行化的优化算法当运行于集群机时,加速比随着核数(8~512核)的增加而递增,当核数为512时,加速比达到最大值(310);并行效率随着核数的增加逐渐下降,最高值0.91,最低值0.61,取得了明显的加速效果。

Parallelization of an Optimization Algorithm for Beneficial Watershed Management Practices

The optimization of beneficial management practices (or beneficial management practices, BMPs) is a typical case of complex geo-computation; a computation-intensive search for optimal solutions of watershed BMPs through many iterative watershed model simulations. This paper presents a parallelization of the epsilon non-dominated sorted genetic algorithm (ε-NSGA-Ⅱ), an increasingly widely-used algorithm for BMPs optimization. The proposed parallel optimization algorithm was designed based on a master-slave parallelization strategy and implemented using the message passing interface (MPI). A case study executed on an IBM cluster for the Meichuan Jiang watershed (about 6366 km²) in the Lake Poyang basin shows that the proposed parallel BMPs optimization algorithm performs well. When the count of cores used in the case study increased (8~512 cores), the proposed parallel optimization algorithm delivered a higher speedup ratio. The speedup ratio reached 310 when 512 cores were used. In this case study, the parallel efficiency of the proposed parallel BMPs optimization algorithm decreased with an increase of the count of cores. The parallel efficiency ranged from 0.61 to 0.91, demonstrating that the proposed algorithm achieves good parallel performance.