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Practical application of penalty-free evolutionary multi-objective optimisation of water distribution systems

机译:供水系统无罚进化多目标优化的实际应用

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摘要

Evolutionary algorithms are a commonly applied optimisation approach in water distribution systems. However, the algorithms are time consuming when applied to large optimisation problems. The aim of this paper is to evaluate the application of a penalty-free multi-objective evolutionary optimisation algorithm to solve a real-world network design problem. The optimization model uses pressure-dependent analysis that accounts for the pressure dependency of the nodal flows and thus avoids the need for penalties to address violations of the nodal pressure constraints. The algorithm has been tested previously using benchmark optimisation problems in the literature. In all cases, the algorithm found improved solutions and/or the best solution reported previously in the literature with considerably fewer function evaluations. In this paper, a real-world network with over 250 pipes was considered. The network comprises multiple sources, multiple demand categories, many fire flows and involves extended period simulation. Due to the size and complexity of the optimization problem, a high performance computer that comprises multiple cores was used for the computational solution. Multiple optimisation runs were performed concurrently. Overall, the algorithm performs well; it consistently provides least cost solutions that satisfy the system requirements quickly. The least-cost design obtained was over 40% cheaper than the existing network in terms of the pipe costs.
机译:进化算法是水分配系统中常用的优化方法。但是,将算法应用于大型优化问题时非常耗时。本文的目的是评估一种无惩罚的多目标进化优化算法在解决实际网络设计问题中的应用。优化模型使用压力相关分析,该分析考虑了节点流的压力相关性,因此避免了为解决违反节点压力约束而需要进行惩罚的问题。该算法先前已使用文献中的基准优化问题进行过测试。在所有情况下,该算法都能找到改进的解决方案和/或先前文献中报告的最佳解决方案,而功能评估却要少得多。在本文中,考虑了具有超过250个管道的真实网络。该网络包括多个来源,多个需求类别,许多火源,并涉及长时间仿真。由于优化问题的规模和复杂性,包含多核的高性能计算机被用于计算解决方案。同时执行多个优化运行。总体而言,该算法表现良好;它始终如一地提供成本最低的解决方案,可以快速满足系统要求。就管道成本而言,获得的成本最低的设计比现有网络便宜40%以上。

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