协方差矩阵自适应贪婪搜索在给水管网优化中的应用
Covariance Matrix Adaptation Greedy Search Applied to Water Distribution System Optimization
摘要：
配水系统设计是一个具有挑战性的优化问题，具有大量的搜索维度和约束条件。通过这种方式，进化算法（EA）已被广泛应用于优化WDS，以最大限度地减少成本，同时满足压力约束。本文提出了一种新的混合进化框架，包括三个不同的阶段。第一阶段应用CMA-ES，这是一种用于连续优化的鲁棒自适应元启发式算法。这之后是向上贪婪搜索阶段，以消除压力违规。最后，使用向下贪婪搜索阶段来减少过大的管道。为了评估混合方法的有效性，它被应用到五个著名的WDS的案例研究。结果表明，新的框架优于CMA-ES本身和其他以前应用的算法在大多数基准的优化速度和网络成本。
英文摘要：
Water distribution system design is a challenging optimisation problem with a high number of search dimensions and constraints. In this way, Evolutionary Algorithms (EAs) have been widely applied to optimise WDS to minimise cost subject whilst meeting pressure constraints. This paper proposes a new hybrid evolutionary framework that consists of three distinct phases. The first phase applied CMA-ES, a robust adaptive meta-heuristic for continuous optimisation. This is followed by an upward-greedy search phase to remove pressure violations. Finally, a downward greedy search phase is used to reduce oversized pipes. To assess the effectiveness of the hybrid method, it was applied to five well-known WDSs case studies. The results reveal that the new framework outperforms CMA-ES by itself and other previously applied heuristics on most benchmarks in terms of both optimisation speed and network cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
