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Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge

机译:结合领域知识改进供水系统设计的遗传算法优化

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Over the last two decades, evolutionary algorithms (EAs) have become a popular approach for solving water resources optimization problems. However, the issue of low computational efficiency limits their application to large, realistic problems. This paper uses the optimal design of water distribution systems (WDSs) as an example to illustrate how the efficiency of genetic algorithms (GAs) can be improved by using heuristic domain knowledge in the sampling of the initial population. A new heuristic procedure called the Prescreened Heuristic Sampling Method (PHSM) is proposed and tested on seven WDS cases studies of varying size. The EPANet input files for these case studies are provided as supplementary material. The performance of the PHSM is compared with that of another heuristic sampling method and two non-heuristic sampling methods. The results show that PHSM clearly performs bet overall, both in terms of computational efficiency and the ability to find near-optimal solutions. In addition, the relative advantage of using the PHSM increases with network size. (C) 2014 Elsevier Ltd. All rights reserved.
机译:在过去的二十年中,进化算法(EA)已成为解决水资源优化问题的流行方法。但是,计算效率低的问题将它们的应用限制在较大的现实问题上。本文以供水系统(WDS)的优化设计为例,说明如何通过在初始人口抽样中使用启发式领域知识来提高遗传算法(GA)的效率。提出了一种新的启发式程序,称为预筛选启发式采样方法(PHSM),并在七个不同规模的WDS案例研究中进行了测试。这些案例研究的EPANet输入文件作为补充材料提供。将PHSM的性能与另一种启发式采样方法和两种非启发式采样方法进行了比较。结果表明,PHSM在计算效率和找到接近最优解的能力方面都清楚地在整体上押注。此外,使用PHSM的相对优势随网络规模的增加而增加。 (C)2014 Elsevier Ltd.保留所有权利。

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