IEEE Trans Cybern. 2023 Mar;53(3):1667-1681. doi: 10.1109/TCYB.2021.3107900. Epub 2023 Feb 15.
Evolutionary computation (EC) algorithms have been successfully applied to the small-scale water distribution network (WDN) optimization problem. However, due to the city expansion, the network scale grows at a fast speed so that the efficacy of many current EC algorithms degrades rapidly. To solve the large-scale WDN optimization problem effectively, a two-stage swarm optimizer with local search (TSOL) is proposed in this article. To address the issues caused by the large-scale and multimodal characteristics of the problem, the proposed algorithm divides the optimization process into an exploration stage and an exploitation stage. It first finds a promising region of the search space in the exploration stage. Then, it searches thoroughly in the promising region to obtain the final solution in the exploitation stage. To search effectively the huge search space, we propose an improved level-based learning optimizer and use it in both the exploration and exploitation stages. Two new local search algorithms are proposed to further improve the quality of the solution. Experiments on both synthetic benchmark networks and a real-world network show that the proposed algorithm has outperformed the state-of-the-art metaheuristic algorithms.
进化计算 (EC) 算法已成功应用于小规模供水管网 (WDN) 优化问题。然而，由于城市的扩张，管网规模迅速扩大，许多现有的 EC 算法的效率迅速降低。为了有效解决大规模 WDN 优化问题，本文提出了一种具有局部搜索的两阶段群体优化器 (TSOL)。为了解决该问题由于大规模和多模态特征所带来的问题，所提出的算法将优化过程分为探索阶段和开发阶段。它首先在探索阶段找到搜索空间的一个有希望的区域。然后，它在有希望的区域中进行深入搜索，以在开发阶段获得最终解决方案。为了有效地搜索巨大的搜索空间，我们提出了一种改进的基于层次的学习优化器，并将其应用于探索和开发阶段。还提出了两种新的局部搜索算法，以进一步提高解决方案的质量。在合成基准网络和真实网络上的实验表明，所提出的算法优于最先进的元启发式算法。
