论文标题
增强的基于模拟的迭代局部搜索元启发式式水分配网络设计优化
An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization
论文作者
论文摘要
重力喂养水分配网络设计（WDND）优化问题包括确定水网络的管道直径，使水力限制得到满足并最小化。传统上，此类设计决定是根据专家经验做出的。但是，当网络的大小增加时，经验法则很少会导致几乎最佳的决定。在过去的三十年中，已经开发了大量技术来解决最佳设计水分配网络的问题。在本文中，我们在多周期设置中解决了时间变化的需求模式，解决了NP-HARD供水网络设计（WDND）优化问题。我们提出了一种新的基于仿真的迭代局部搜索元启发式化，以进一步探索问题的结构，以获取高质量的解决方案。计算实验表明，我们的方法非常有竞争力，因为它能够在大多数执行的测试中改进最先进的元启发式化。此外，它会使低成本解决方案的收敛速度更快，并证明了更强大的性能，因为它与最著名的解决方案相比较小。
The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. In this paper, we tackle the NP-hard water distribution network design (WDND) optimization problem in a multi-period setting where time varying demand patterns occur. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions. Computational experiments show that our approach is very competitive as it is able to improve over a state-of-the-art metaheuristic for most of the performed tests. Furthermore, it converges much faster to low cost solutions and demonstrates a more robust performance in that it obtains smaller deviations from the best known solutions.
