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Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

机译:基于伪遗传算法和遗传算子敏感性的供水管网设计

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摘要

Genetic algorithms (GA) are optimization techniques that are widely used in the design of water distribution networks. One of the main disadvantages of GA is positional bias, which degrades the quality of the solution. In this study, a modified pseudo-genetic algorithm (PGA) is presented. In a PGA, the coding of chromosomes is performed using integer coding; in a traditional GA, binary coding is utilized. Each decision variable is represented by only one gene. This variation entails a series of special characteristics in the definition of mutation and crossover operations. Some benchmark networks have been used to test the suitability of a PGA for designing water distribution networks. More than 50,000 simulations were conducted with different sets of parameters. A statistical analysis of the obtained solutions was also performed. Through this analysis, more suitable values of mutation and crossover probabilities were discovered for each case. The results demonstrate the validity of the method. Optimum solutions are not guaranteed in any heuristic method. Hence, the concept of a "good solution" is introduced. A good solution is a design solution that does not substantially exceed the optimal solution that is obtained from the simulations. This concept may be useful when the computational cost is critical. The main conclusion derived from this study is that a proper combination of population and crossover and mutation probabilities leads to a high probability that good solutions will be obtained.
机译:遗传算法(GA)是优化技术,广泛用于配水管网的设计。 GA的主要缺点之一是位置偏差,这会降低解决方案的质量。在这项研究中,提出了一种改进的伪遗传算法(PGA)。在PGA中,染色体的编码是使用整数编码来完成的。在传统的遗传算法中,使用二进制编码。每个决策变量仅由一个基因表示。这种变化在突变和交叉操作的定义中需要一系列特殊的特征。一些基准网络已用于测试PGA在设计供水网络方面的适用性。使用不同的参数集进行了50,000多次仿真。还对获得的溶液进行了统计分析。通过该分析,发现了每种情况下更合适的突变和交叉概率值。结果证明了该方法的有效性。任何启发式方法都无法保证最佳解决方案。因此,引入了“好的解决方案”的概念。一个好的解决方案是一种设计解决方案,该解决方案基本上不超过从仿真中获得的最佳解决方案。当计算成本至关重要时,此概念可能会很有用。这项研究得出的主要结论是,种群,交叉和突变概率的适当组合会导致获得良好解决方案的可能性很高。

著录项

  • 来源
    《Water Resources Management》 |2013年第12期|4149-4162|共14页
  • 作者单位

    Departamcnto de Ingenieria Hidraulica y Medio Ambiente, Universitat Politecnica de Valencia, Valencia, Espana;

    Departamcnto de Ingenieria Hidraulica y Medio Ambiente, Universitat Politecnica de Valencia, Valencia, Espana;

    Departamcnto de Ingenieria Hidraulica y Medio Ambiente, Universitat Politecnica de Valencia, Valencia, Espana;

    Departamcnto de Ingenieria Hidraulica y Medio Ambiente, Universitat Politecnica de Valencia, Valencia, Espana;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Genetic algorithms; Design; Water networks; Optimization;

    机译:遗传算法;设计;水网络;优化;

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