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A novel cellular automata based approach to storm sewer design

机译:一种基于元胞自动机的新型雨水管道设计方法

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Optimal storm sewer design aims at minimizing capital investment on infrastructure whilst ensuring good system performance under specified design criteria. An innovative sewer design approach based on cellular automata (CA) principles is introduced in this paper. Cellular automata have been applied as computational simulation devices in various scientific fields. However, some recent research has indicated that CA can also be a viable and efficient optimization engine. This engine is heuristic and largely relies on the key properties of CA: locality, homogeneity, and parallelism. In the proposed approach, the CA-based optimizer is combined with a sewer hydraulic simulator, the EPA Storm Water Management Model (SWMM). At each optimization step, according to a set of transition rules, the optimizer updates all decision variables simultaneously based on the hydraulic situation within each neighbourhood. Two sewer networks (one small artificial network and one large real network) have been tested in this study. The CA optimizer demonstrated its ability to obtain near-optimal solutions in a remarkably small number of computational steps in a comparison of its performance with that of a genetic algorithm.
机译:最佳的雨水管道设计旨在最大程度地减少对基础架构的资本投资,同时确保在指定的设计标准下保持良好的系统性能。本文介绍了一种基于元胞自动机(CA)原理的创新下水道设计方法。元胞自动机已在各种科学领域中用作计算仿真设备。但是,最近的一些研究表明,CA也是可行且有效的优化引擎。该引擎是启发式的,在很大程度上依赖于CA的关键特性:局部性,同质性和并行性。在提出的方法中,基于CA的优化器与下水道液压模拟器EPA雨水管理模型(SWMM)结合在一起。在每个优化步骤中,根据一组过渡规则,优化器会根据每个社区内的水力状况同时更新所有决策变量。这项研究测试了两个下水道网络(一个小型​​人工网络和一个大型真实网络)。通过将CA优化器的性能与遗传算法的性能进行比较,证明了它能够以极少的计算步骤获得接近最佳解的能力。

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