School of Computer Science, China University of Geosciences, Wuhan, 430074, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
Environ Sci Pollut Res Int. 2021 Sep;28(34):46266-46280. doi: 10.1007/s11356-021-13352-4. Epub 2021 Mar 27.
The real-time location of pollution sources is the process of inverting pollution sources based on the dynamic optimization model constructed by the time-varying pollution concentration detected by the water quality sensor. Due to the vast quantities of the water supply networks, the water quality sensors will only be placed on critical nodes, resulting in multiple solutions. However, the increased monitoring data enhances the uniqueness of the solution. Combined with the real-time location of pollution sources, this work proposed a multi-strategy dynamic multi-mode optimization algorithm based on domain knowledge, which could guide the population search and avoid trapped into local optimal. The merging mechanism was used to keep the diversity of the population and prevent sub-population clustering on the same optimal solution. The simulation results showed that the algorithm could effectively solve the real-time location problem of pollution sources in different pipe networks and pollution scenarios.
污染源实时定位是根据水质传感器检测到的时变污染浓度构建的动态优化模型来反演污染源的过程。由于供水管网数量庞大，水质传感器只会放置在关键节点上，因此会产生多个解。但是，增加监测数据可以增强解决方案的独特性。结合污染源实时定位，本文提出了一种基于领域知识的多策略动态多模式优化算法，它可以指导种群搜索，避免陷入局部最优。采用合并机制保持种群的多样性，防止子种群在同一最优解上聚类。仿真结果表明，该算法能够有效解决不同管网和污染场景下的污染源实时定位问题。
