Wu Qinghua, Wu Bin, Yan Xuesong
Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430205 China.
School of Economics and Management, Nanjing Tech University, Najing, 211816 China.
Neural Comput Appl. 2023;35(3):2059-2076. doi: 10.1007/s00521-022-07002-0. Epub 2022 Feb 22.
Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source's characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution's real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes' distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions' number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm's historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm's effectiveness in solving problems-accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.
饮用水安全是当前全社会高度重视的安全问题。对于突发性水污染事故，有必要实时追踪水污染源头，确定污染源特征信息，为应急管理部门决策提供技术支持。水污染实时溯源存在如下问题：污染源的非唯一性和动态实时性。针对这两个难点，本研究设计并提出了一种基于动态多模式优化的智能溯源算法。作为一个多模式优化问题，污染溯源可能有多个相似的最优解。首先，新算法通过最优子种群划分策略合理划分种群，使单个子种群内节点分布更相似，有利于局部优化。然后，采用相似峰值惩罚策略消除相似解，减少非唯一解数量，因为实时溯源比传统离线溯源需要更高的算法收敛性，且参数变化、历史信息保存和自适应初始化策略等动态问题能够合理利用算法历史知识，在问题发生变化时改善种群空间，提高种群收敛速度。实验结果表明，所提出的新算法在解决问题方面有效——能够准确追踪污染源，并在短时间内获取相应特征信息。
