本研究讨论了遗传算法（GA）优化，一种功能强大、实用的方法 该工具将帮助公用事业公司面临如何满足未来需求的艰难决策 发育遗传优化可以用来确定一系列可行的、低成本的算法 满足特定设计和性能标准的解决方案。遗传算法分析 标准配电系统建模与方案评估 方法，并提供最小化项目生命周期成本的额外好处。 这项研究描述了德克萨斯州大草原的一个案例研究，展示了 一个城市使用遗传算法优化来确定最佳的总体供水系统扩建 计划满足未来的需求。考虑位置等变量的遗传算法优化 以及新管道和仓库的尺寸；泵和流量控制的设置，以及 减压阀；可能消除现有控制阀、储罐、， 和泵站；以及不同水源的供水率选择 连接。该模型不仅优化了计划资本改进（新 管道、储罐、泵和阀门）以及操作设定点和时间表 用于调节阀和泵送操作。遗传算法优化可以实现 资本成本节省20-30%。包括12个参考文献、表格、图表。

This study discusses genetic algorithm (GA) optimization, a powerful, practical tool that would assist utilities facing hard decisions about how to meet future growth. GA optimization can be used to identify a range of feasible, low-cost solutions that satisfy specific design and performance criteria. GA analysis fits into the standard distribution system modeling and alternatives evaluation approach and offers the added benefit of minimizing a project's life-cycle costs. This study describes a case study from Grand Prairie, Texas, that demonstrates how one city used GA optimization to identify the best overall water system expansion plan to meet future needs. GA optimization considered such variables as location and size of new pipes and storage; settings of pumps and flow control and pressure-reducing valves; possible elimination of existing control valves, tanks, and pump stations; and, the choice of supply rates from different water source connections. The model optimized not only planned capital improvements (new pipes, tanks, pumps, and valves) but also operational set points and schedules for regulating valves and pumping operations. GA optimization regularly achieves capital cost savings of 20-30%. Includes 12 references, table, figures.